CN113657951A

CN113657951A - Commodity recommendation method and device, and commodity release processing method and device

Info

Publication number: CN113657951A
Application number: CN202010398198.9A
Authority: CN
Inventors: 乔光; 孙伟; 陈中强; 陈巧敏
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2020-05-12
Filing date: 2020-05-12
Publication date: 2021-11-16

Abstract

The application provides a commodity recommendation method and device and a commodity release processing method and device, wherein the commodity recommendation method comprises the following steps: acquiring commodity elements submitted by a merchant aiming at a target commodity; screening a target user group corresponding to the commodity element from the user group according to the user group corresponding to the pre-acquired user data; integrating the commodity data of the target user group in the user data to obtain the commodity preference distribution of the target user group; and determining and recommending the recommended commodities in the commodity set to which the target commodity belongs based on the commodity preference distribution and the commodity elements.

Description

Commodity recommendation method and device, and commodity release processing method and device

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for recommending a commodity and a method and an apparatus for issuing a commodity.

Background

With the development of internet technology, online sales patterns are accepted by more and more people, and the number of the coming users increases, so that how to reach more users becomes the most concerned problem of merchants of online stores. However, both the research and search methods have the problems of low timeliness and high use cost, and the effect of tracking the commodities cannot be realized after the commodities are released or the sales strategies are adjusted according to the needs of the users, so an effective scheme for realizing the effect of low cost and high reach of the users is needed.

Disclosure of Invention

In view of this, the present application provides a commodity recommendation method. The application also relates to a commodity recommending device, a commodity release processing method, a commodity release processing device, two kinds of computing equipment and two kinds of computer readable storage media, so as to solve the technical defects in the prior art.

According to a first aspect of an embodiment of the present application, there is provided a commodity recommendation method, including:

acquiring commodity elements submitted by a merchant aiming at a target commodity;

screening a target user group corresponding to the commodity element from the user group according to the user group corresponding to the pre-acquired user data;

integrating the commodity data of the target user group in the user data to obtain the commodity preference distribution of the target user group;

and determining and recommending the recommended commodities in the commodity set to which the target commodity belongs based on the commodity preference distribution and the commodity elements.

Optionally, the commodity element is submitted by the merchant by triggering an element identifier displayed by the commodity element in a preset commodity element library on a triggering display interface;

and displaying the element identifiers of the commodity elements in the commodity element library on the trigger display interface according to the element grades to which the commodity elements belong.

Optionally, a user-defined element control is displayed on the trigger display interface, and if the user-defined element control is triggered, the element similarity between a user-defined commodity element and a commodity element in the commodity element library is calculated, and whether a commodity element with the element similarity between the user-defined commodity element and the commodity element in the commodity element library being greater than a preset similarity threshold exists is judged;

and if so, taking the commodity element with the highest element similarity with the user-defined commodity element as the commodity element submitted by the merchant.

Optionally, if the determination result obtained after the step of determining whether there is a commodity element in the commodity element library whose element similarity with the user-defined commodity element is greater than the preset similarity threshold is non-existent, the following operations are performed:

judging whether the user-defined commodity elements meet the adding conditions for adding into the commodity element library or not;

and if so, adding the user-defined commodity elements into the commodity element library.

Optionally, the screening, according to a user group corresponding to pre-obtained user data, a target user group corresponding to the commodity element from the user group includes:

calculating the attention matching degree of the users in the user group and the target commodity according to the commodity brand of the merchant of the target commodity, the commodity elements and the commodity data of the users in the user group, wherein the commodity data comprises the commodity brand of the user group;

and selecting users with the attention matching degree higher than a preset matching degree threshold value from the user group as target users to form the target user group.

Optionally, the integrating the commodity data of the target user group in the user data to obtain the commodity preference distribution of the target user group includes:

integrating the attention data contained in the commodity data of the target user group to obtain commodity elements of which the attention degrees of the target user group meet preset conditions;

and calculating the attention number and the attention proportion of the target user group to each integrated commodity element to serve as commodity preference distribution of the target user group.

Optionally, the step of screening a target user group corresponding to the commodity element from the user group according to a user group corresponding to pre-obtained user data, the step of integrating commodity data of the target user group from the user data to obtain commodity preference distribution of the target user group, and the step of determining and recommending a recommended commodity in a commodity set to which the target commodity belongs based on the commodity preference distribution and the commodity element are realized based on a commodity recommendation model;

the input of the commodity recommendation model comprises the commodity elements, and the user data is called as input;

and outputting the recommended commodity.

Optionally, the user data includes at least one of the following:

user purchasing behavior data, commodity data, third party attention data and user attribute data;

wherein the user purchase behavior data comprises at least one of: shopping channel, shopping frequency and shopping preference;

the commodity data includes at least one of: click data, sales data, browse data;

the third party attention data comprises at least one of: the attention behavior data of the user in the third-party application;

the user attribute data comprises at least one of: age, economic status, consumption category.

According to a second aspect of embodiments of the present application, there is provided an article recommendation device including:

the acquisition module is configured to acquire the commodity elements submitted by the merchants aiming at the target commodities;

the screening module is configured to screen a target user group corresponding to the commodity element from the user group according to the user group corresponding to the pre-acquired user data;

the integration module is configured to integrate the commodity data of the target user group in the user data to obtain commodity preference distribution of the target user group;

and the determining module is configured to determine and recommend the recommended commodities in the commodity set to which the target commodities belong based on the commodity preference distribution and the commodity elements.

According to a third aspect of the embodiments of the present application, there is provided a commodity release processing method, including:

receiving a commodity issuing request submitted by a merchant;

reading commodity elements in a commodity element library based on the commodity release request and displaying the commodity elements to the merchant;

receiving a target commodity element selected by the merchant from the displayed commodity elements;

determining the commodity preference distribution of the sub-user groups corresponding to the target commodity elements in the user groups; the user group is determined according to user data acquired in advance;

and generating commodity issuing information of the merchant and sending the commodity issuing information to the merchant based on the commodity preference distribution and the target commodity elements.

Optionally, the determining of the distribution of the commodity preference of the sub-user group corresponding to the target commodity element in the user group includes:

screening a sub-user group corresponding to the target commodity element from the user group;

and integrating the commodity data of the sub-user group in the user data to obtain the commodity preference distribution of the sub-user group.

Optionally, the screening, from the user group, a sub-user group corresponding to the target commodity element includes:

calculating the attention matching degree of the users in the user group and the target commodity element according to the commodity brand of the merchant, the commodity element and the commodity data of the users in the user group contained in the user data;

and selecting users with attention matching degrees higher than a preset matching degree threshold value from the user group to form the sub-user group.

Optionally, the integrating the commodity data of the sub-user group in the user data to obtain the commodity preference distribution of the sub-user group includes:

Optionally, the commodity element is displayed to the merchant through a trigger display interface, and the merchant submits the target commodity element through an element identifier which triggers the display of the trigger display interface;

and if so, taking the commodity element with the highest element similarity with the user-defined commodity element as the target commodity element selected by the merchant.

Optionally, the commodity release information includes at least one of the following:

and updating element information according to the commodity preference distribution and the commodity information determined by the target commodity element based on the commodity preference distribution and the brand information of the joint brand determined by the target commodity element.

Optionally, after the step of generating the commodity release information of the merchant and sending the commodity release information to the merchant is executed based on the commodity preference distribution and the target commodity element, the method further includes:

collecting commodity data of a target commodity issued by the merchant;

wherein the commodity data comprises at least one of: click data, sales data, browse data.

Optionally, the user data includes at least one of the following:

According to a fourth aspect of the embodiments of the present application, there is provided a commodity distribution processing apparatus including:

the receiving module is configured to receive a commodity issuing request submitted by a merchant;

the reading module is configured to read the commodity elements in the commodity element library and display the commodity elements to the merchant based on the commodity issuing request;

a selection module configured to receive a target merchandise element selected by the merchant among the merchandise elements displayed;

a determining module configured to determine a commodity preference distribution of a sub-user group corresponding to the target commodity element in a user group; the user group is determined according to user data acquired in advance;

and the generating module is configured to generate and send commodity publishing information of the merchant to the merchant based on the commodity preference distribution and the target commodity elements.

According to a fifth aspect of embodiments herein, there is provided a first computing device comprising:

a memory and a processor;

the memory is to store computer-executable instructions, and the processor is to execute the computer-executable instructions to:

According to a sixth aspect of embodiments herein, there is provided a second computing device comprising:

a memory and a processor;

receiving a commodity issuing request submitted by a merchant;

According to a seventh aspect of embodiments of the present application, there is provided a first computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the item recommendation method.

According to an eighth aspect of the embodiments of the present application, there is provided a second computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the commodity release processing method.

According to the commodity recommendation method provided by the embodiment of the application, after the commodity elements submitted by a merchant for the target commodities are obtained, the target user groups corresponding to the commodity elements are screened out according to the user groups corresponding to the pre-obtained user data, then the commodity data corresponding to the target user groups are integrated to obtain the commodity preference distribution of the target user groups, and finally, based on the commodity preference distribution and the commodity elements, the recommended commodities are determined in the commodity set to which the target commodities belong for recommendation, so that the recommended commodities can be determined quickly and accurately based on the needs of the merchant, the merchant can trigger more users through the recommended commodities, the consumption cost of determining the recommended commodities by the merchant is reduced, and the experience of the merchant is improved to a great extent.

According to the commodity issuing processing method provided by the embodiment of the application, after a commodity issuing request submitted by a merchant is received, commodity elements in a commodity element library are read for display based on the commodity issuing request, the commodity preference distribution of the target commodity elements in a user group is determined based on the target commodity elements selected by the merchant, and finally, commodity issuing information of the merchant is generated and transmitted back to the merchant based on the commodity preference distribution and the target commodity elements.

Drawings

Fig. 1 is a flowchart of a commodity recommendation method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a trigger show interface according to an embodiment of the present application;

FIG. 3 is a schematic diagram of a distribution of product preferences according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a recommended goods page provided by an embodiment of the present application;

FIG. 5 is a schematic diagram of a feedback page provided by an embodiment of the present application;

fig. 6 is a schematic structural diagram of a commodity recommending apparatus according to an embodiment of the present application;

fig. 7 is a flowchart of a method for processing a commodity release according to an embodiment of the present application;

fig. 8 is a flowchart of a process applied to a commodity release scenario according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a product distribution processing apparatus according to an embodiment of the present application;

FIG. 10 is a block diagram of a first computing device according to an embodiment of the present application;

fig. 11 is a block diagram of a second computing device according to an embodiment of the present application.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.

The terminology used in the one or more embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the present application. As used in one or more embodiments of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present application refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments of the present application to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first aspect may be termed a second aspect, and, similarly, a second aspect may be termed a first aspect, without departing from the scope of one or more embodiments of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

In the present application, a merchandise recommendation method is provided. The present application also relates to a commodity recommendation apparatus, a commodity distribution processing method, a commodity distribution processing apparatus, two kinds of computing devices, and two kinds of computer-readable storage media, which are described in detail in the following embodiments one by one.

The embodiment of the commodity recommendation method provided by the application is as follows:

fig. 1 shows a flowchart of a product recommendation method according to an embodiment of the present application, which specifically includes the following steps:

and step S102, acquiring the commodity elements submitted by the merchant aiming at the target commodity.

In practical application, before issuing a new commodity or before adjusting a sales strategy of a currently sold commodity, a merchant needs to investigate the requirements of a current user, and then determines the issuing strategy or the adjusting strategy of the new commodity according to the investigation result, so that the number of reaching users is increased; however, the process from the beginning of research to the determination of the strategy takes much time and is costly, and although more users can be reached, the timeliness is low, and the effect is very little in the application scenario that the commodity is updated and updated quickly.

In order to reduce the cost of the merchant and simultaneously realize quick and accurate determination of recommended commodities, after acquiring the commodity elements submitted by the merchant for the target commodities, screening out a target user group corresponding to commodity elements according to a user group corresponding to pre-acquired user data, then integrating commodity data corresponding to the target user group to obtain commodity preference distribution of the target user group, and finally based on the commodity preference distribution and the commodity elements, the recommended commodities are determined and recommended in the commodity set to which the target commodity belongs, so that the recommended commodities can be determined quickly and accurately based on the requirements of merchants, the merchants can trigger more users through the recommended commodities, and the consumption cost of determining recommended commodities by the merchant is reduced, and the experience of the merchant is improved to a great extent.

In specific implementation, the merchant specifically refers to a producer or a seller providing goods, and correspondingly, the target goods may be goods which are not issued by the merchant, or goods which have been issued by the merchant and have a poor selling effect, or goods which are not produced; the commodity elements specifically refer to attribute information of the target commodity in each attribute dimension, and for example, colors, materials, or shapes of the commodity may be referred to as commodity elements of the commodity.

In practical application, under the condition that the commodity elements submitted by the merchant for the target commodities are obtained, the merchant has the intention of determining the user demands, and the intention is shown in that the merchant may need to issue new commodities, or in that the merchant is not satisfied with the sales condition of the commodities currently sold, or in that the merchant needs to produce commodities close to the user demands; based on the above, the recommended commodities in the commodity set to which the target commodity belongs are determined subsequently according to the commodity elements, so that the recommended commodities which can be effectively triggered can be recommended to the merchant, and the commodity sales rate or the use rate of the merchant is improved.

Further, in order to accurately determine the recommended commodity subsequently, the accurate acquisition of the commodity element of the target commodity is needed to be realized, and the merchant is required to describe the commodity element of the target commodity by himself or herself due to the fact that the target commodities of different merchants are different, so that the accurate determination of the recommended commodity subsequently can be realized, and in order to further improve the experience of the merchant and facilitate the operation of the merchant, in this embodiment, the commodity element can be submitted by the merchant by triggering an element identifier displayed by the commodity element in a pre-configured commodity element library on a triggering display interface; and the element identifiers of the commodity elements in the commodity element library are displayed on the triggering display interface according to the element grades to which the commodity elements belong.

Specifically, the commodity element library comprises a large number of commodity elements, and the commodity elements are displayed in a manner of element identification on a triggering display interface for a merchant to select; the element identifier displayed by the triggering display interface specifically refers to a page which can be viewed by a merchant, and the display identifier corresponding to each basic commodity element in the commodity element library can be displayed to the merchant, so that the merchant can determine the commodity element of the target commodity by checking or clicking each element identifier, and it needs to be noted that all commodity elements corresponding to all element identifiers submitted by the merchant are the commodity elements of the target commodity.

Moreover, since there are many basic commodity elements contained in the commodity element library, it may not be convenient for a merchant to perform check-up or click if the element identifiers corresponding to all the basic commodity elements are displayed at one time through the trigger display interface, and it is also difficult to query the element identifiers corresponding to the target commodity in the large number, as shown in fig. 2, the commodity elements in the commodity element library may be divided according to a hierarchy by a hierarchical mapping relationship, where a first hierarchy may be a category item, a second hierarchy is an attribute item under the category item, and a third hierarchy is an attribute value item under the attribute item;

the method comprises the steps that a type item can exist before a category item, after a merchant selects the type item of a target commodity, all the category items under the type item are displayed through triggering a page corresponding to a display interface, after the category item is selected again, all attribute items under the category item are displayed, after the attribute item is selected again, all attribute values under the attribute item are displayed, finally commodity elements of the target commodity are integrated according to the selected attribute value of the merchant, and in addition, a plurality of selectable items in any level can be selected simultaneously.

Referring to fig. 2, in the case that the merchant is a merchant selling men's clothing, at this time, the merchant selects men's clothing items in the display page, sequentially displays all attribute items under the men's clothing items, the merchant selects "color item", "fabric item", and "trend element item", and respectively selects "electro-optic blue", "frosted", and "asymmetric" under each attribute item, and then it is determined that the commodity elements corresponding to the target commodity of the merchant are respectively "men's clothing", "electro-optic blue", "frosted", and "asymmetric", so as to subsequently determine recommended commodities for recommendation.

Through providing the trigger display interface for the merchant, the merchant can accurately determine the commodity elements of the merchant while facilitating the operation of the merchant, so that the accuracy of determining recommended commodities can be improved, and more users can be effectively touched by the merchant.

In addition, in order to facilitate the operation of the merchant and avoid the lack of the commodity elements of the target commodity which conforms to the merchant in the commodity element library, in this embodiment, a custom element control is displayed on the trigger display interface, and if the custom element control is triggered, the element similarity between the custom commodity elements and the commodity elements in the commodity element library is calculated, and whether the commodity elements of which the element similarity with the custom commodity elements is greater than a preset similarity threshold exist in the commodity element library is judged;

if so, taking the commodity element with the highest element similarity with the user-defined commodity element as the commodity element submitted by the merchant;

if not, judging whether the user-defined commodity elements meet the adding conditions for adding into the commodity element library or not;

if yes, adding the self-defined commodity element into the commodity element library;

if not, no processing is carried out.

Specifically, the custom element control displayed by the trigger display interface is an interface for facilitating a merchant to perform custom description on a target commodity, and if the merchant does not find a commodity element conforming to the target commodity in the trigger display interface, the custom commodity element can be uploaded by triggering the custom element control, and at this time, the element similarity between the custom commodity element and the commodity element in the commodity element library needs to be calculated, so that whether the custom commodity element exists in the commodity element library or not is detected;

based on this, the specific detection means is a mode of judging whether the element similarity is greater than a preset similarity threshold, if so, the commodity element corresponding to the user-defined commodity element exists in the commodity element library, and the commodity element with the highest element similarity to the user-defined commodity element is taken as the commodity element submitted by the merchant; if not, the commodity element corresponding to the user-defined commodity element does not exist in the commodity element library, and at the moment, the user-defined commodity element can be used as the commodity element submitted by the merchant; whether the user-defined commodity elements meet the adding conditions of adding the commodity element library or not needs to be detected; if yes, adding the self-defined commodity element into the commodity element library; if not, no processing is carried out.

The adding condition can be to detect whether the expression meaning of the vocabulary corresponding to the customized commodity element is the commodity description meaning or not, or to detect whether the expression meaning of the vocabulary corresponding to the customized commodity element is correct or not, or to detect whether the format of the customized commodity element is correct or not, so as to realize the detection of the customized commodity element, and further ensure the standard degree of the commodity elements in the commodity element library.

Along with the above example, when the men's clothing merchant performs the check-in of the commodity element of the target commodity through the page shown in fig. 2, it is found that there is no men's clothing type attribute item in the page, at this time, the merchant checks-in the self-defined element control for the entry of the self-defined commodity element, the entered self-defined commodity element is the "hat shirt", at this time, the element similarity between the "hat shirt" and each commodity element in the commodity element library is calculated, it is found that the similarity with the "hat shirt" element is the highest is 10%, and is smaller than the preset similarity threshold, it is indicated that there is no "hat shirt" commodity element in the commodity element library, at this time, the "hat shirt" is determined as the commodity element of the target commodity, and the commodity element library is expanded. The expansion of the commodity element library needs to detect whether the 'hat shirt' meets the condition of adding the commodity element library, and under the condition of meeting the condition, the 'hat shirt' is added into the commodity element library.

The merchant submits the commodity elements of the target commodity more conveniently through the custom element control, and after the custom commodity elements are obtained, whether the custom commodity elements can be added into the commodity element library or not is detected through a further judgment mode, so that the purpose of dynamically expanding the commodity element library is achieved, and the merchant can select the commodity elements with high selectivity.

And step S104, according to a user group corresponding to the pre-acquired user data, screening a target user group corresponding to the commodity element from the user group.

Specifically, on the basis of obtaining the commodity elements submitted by the merchant, in order to determine recommended commodities through target commodities and achieve reaching of more users in the crowd corresponding to the target commodities, the target user group corresponding to the commodity elements can be screened out from the user groups corresponding to the user data obtained in advance through the commodity elements; the user group specifically refers to a group formed by users corresponding to the user data which can be acquired, and correspondingly, the target user group specifically refers to a group formed by users corresponding to the commodity elements screened from the user group;

in practical applications, the user data includes at least one of the following: user purchasing behavior data, commodity data, third party attention data and user attribute data; wherein the user purchase behavior data comprises at least one of: shopping channel, shopping frequency and shopping preference; the commodity data includes at least one of: click data, sales data, browse data; the third party attention data comprises at least one of: the attention behavior data of the user in the third-party application; the user attribute data comprises at least one of: age, economic status, consumption category.

In specific implementation, the user data specifically refers to a set of collected data of the user in various aspects, the included user purchasing behavior data specifically refers to data generated when the user purchases a commodity on a trading platform for providing a certain recommended commodity, and the user purchasing behavior data may include a shopping channel, shopping frequency and shopping preference; the commodity data comprises data corresponding to commodities which are concerned by a user or have higher browsing times on the transaction platform, and the commodity data can comprise click data, sales data and browsing data; correspondingly, the third party attention data specifically refers to data corresponding to content which the user pays attention to in the third party application, and the third party attention data may include topics, characters, games and the like which the user pays attention to in the third party application; the user attribute data specifically refers to data corresponding to the user's own attributes, and the user attribute data may include age, economic condition, and consumption category.

Further, in the process of screening the target user group, the accuracy of screening the target user group will affect the subsequently determined recommended goods, and after determining and recommending the recommended goods based on the target goods, the number of users that can be reached is also a certain relation with the target user group, so that the target user group is accurately screened, and the sales rate of the merchants after issuing the recommended goods can be effectively improved, in this embodiment, the process of specifically screening the target user group is as follows:

Specifically, on the basis of obtaining the commodity element of the target commodity, first, a commodity brand of the merchant, and commodity data of the user in the user group corresponding to the commodity element and the user data are determined; secondly, calculating the attention matching degree of each user in the user group and the target commodity based on the commodity brand, the commodity elements and the commodity data; and finally, selecting users higher than a preset matching degree threshold value from the calculated attention matching degrees as target users to form the target user group.

Along the above example, the commodity elements of the target commodity are determined to be { men's clothing, electro-optic blue, frosted sand, asymmetry and hats }, the commodity brand of a men's clothing merchant is a brand A, and determines that the commodity data of each user in the user group includes click data and browsing data, based on the user data providing the trading platform determining the recommended commodity, and at this time, calculating attention matching degrees of the target commodity and each user in the user group based on the commodity elements, the brand A and the commodity data, and the preset matching degree threshold value is 70%, comparing the concerned matching degree obtained by calculation with the preset matching degree threshold value of 70%, determining that 10 users in the user group meet the condition, the 10 groups of users are determined as target users and form a target user group for subsequent accurate determination and recommendation of recommended goods based on the goods preference distribution of the target user group.

By combining the commodity data, the commodity brands and the commodity elements, the attention matching degree of each user in a target commodity user group is calculated, and then the target user group is determined in a mode of comparing with a preset matching degree threshold value, so that the target user group can be accurately determined in the user group, the accuracy of subsequently determining the recommended commodities is promoted, the determined recommended commodities are more consistent with the target user group, more users can be touched if a merchant issues the recommended commodities, and the sales rate of the merchant is improved.

Step S106, integrating the commodity data of the target user group in the user data to obtain the commodity preference distribution of the target user group.

Specifically, on the basis of the target user group selected from the user groups, further, the determined target user group corresponds to the commodity element, so that by determining the commodity preference distribution of the target user group, when a recommended commodity is determined subsequently, the commodity preference distribution can be combined for determination, and the number of target users in the target user group is further increased;

based on this, the commodity data of each target user in the target user group is integrated, and the commodity preference distribution of the target user group is obtained according to the integration result, wherein the commodity preference distribution specifically refers to the distribution condition summarized aiming at the commodity data of each target user in the target user group.

Further, in the process of obtaining the distribution of the commodity preference of the target user group, in order to accurately obtain the distribution of the commodity preference, so that the distribution reflects the preference of the target user group, and the recommended commodity is accurately determined, in this embodiment, the following method is specifically adopted to determine the distribution of the commodity preference:

Specifically, on the basis of determining the commodity data corresponding to each user in the target user group, further determining the attention data corresponding to the target user group based on the commodity data, where the commodity data may refer to the corresponding description above, and this embodiment is not described in detail herein;

based on this, the attention data specifically refers to data related to a commodity which is concerned by each target user in the target user group, the attention data may be data corresponding to a commodity type liked by each target user, data corresponding to a liked commodity shape or data corresponding to a liked commodity color, and the like, and after the attention data is determined, the attention data is integrated to obtain a commodity element of which the attention degree of the target user group meets a preset condition, and the obtained commodity element specifically refers to a commodity element of which the preference of the target user group is higher, which is sorted out through the attention data of the target users in the target user group; and then, calculating attention figures and attention proportions corresponding to the commodity elements obtained after integration, so as to analyze the commodity preference distribution of the target user group.

In practical application, in the process of determining the distribution of the commodity preferences, in order to facilitate the merchant to accurately grasp the preferences of the target user group, after the distribution of the commodity preferences corresponding to the target user group is generated, the merchant is presented through the presentation page shown in fig. 3, so that the merchant can better grasp the preferences of the target user group; the content shown in fig. 3 may be displayed to the merchant by using a bar chart, a sector chart, a polyline chart, and a column chart, and the distribution of the preference of the product may be expressed by using a pictogram, so that the merchant can more intuitively know the preference of the target user group.

According to the above example, after a target user group consisting of 10 ten thousand users is determined, according to commodity data (click data and browse data) of each user in the target user group, attention data of each user is determined, through integration of the attention data, commodity elements with the attention degree meeting preset conditions of the target user group are { men's clothing, hat clothing, electro-optic blue and asymmetry }, then the attention number and the attention proportion of each commodity element are calculated, and the attention number of the men's clothing is determined to be 9.8 ten thousand users, and the attention proportion is 98%; the attention number of the cap blouse is 5 ten thousand users, and the attention proportion is 50%; the attention rate of the electro-optic blue is 1.2 ten thousand users, the attention rate is 12 percent, the attention rate of the asymmetric blue is 0.4 ten thousand users, and the attention rate is 4 percent; based on the determined attention number and attention proportion of each commodity element, commodity preference distribution of the target user group is generated according to the bar-shaped statistical chart, the fan-shaped statistical chart, the broken line statistical chart and the column statistical chart, and the commodity preference distribution is displayed to a men's clothing merchant through a report page so that the merchant can master preference distribution conditions of the users.

In addition, in the process of obtaining the preference distribution of the commodity corresponding to the target user group, in addition to determining the commodity element whose attention degree meets the preset condition through the attention data included in the commodity data, the commodity element whose attention degree meets the preset condition may be determined through click data, sales data or browsing data included in the commodity data, and a specific implementation manner is similar to the description content of the commodity element whose attention degree meets the preset condition through the attention data in this embodiment, which is not described in detail herein.

And S108, determining and recommending recommended commodities in the commodity set to which the target commodities belong based on the commodity preference distribution and the commodity elements.

Specifically, on the basis of determining the commodity preference distribution corresponding to the target user group, the recommended commodities are further determined in a commodity set to which the target commodities belong by combining the commodity preference distribution and the commodity elements, wherein the commodity set is a set which is established in advance and is combined with other commodities with the same type as the target commodities, and the recommended commodities are specifically commodities recommended to a merchant, so that the merchant can reach more users by releasing the recommended commodities, the access rate to the users is improved, and the merchant attracts more users.

It should be noted that the recommended commodity may be a commodity which is not yet produced, a commodity which is already produced but not released, or a commodity which attracts a user with a bad effect after being released, and at this time, the recommended commodity can be determined by the above method, so that more users are reached, and the commodity of the merchant is more in line with the purchase demand of the user.

In specific implementation, when the recommended commodity is a design commodity of a merchant, the merchant is indicated to have the requirement of designing a new commodity, so that commodities reaching more users are obtained; specifically, after the commodity elements submitted by the merchants are obtained, the target user groups corresponding to the commodity elements are screened out from the user groups corresponding to the user data obtained in advance, then the commodity data of the target user groups are integrated, the commodity preference distribution of the target user groups is obtained, finally, based on the commodity elements and the commodity preference distribution submitted by the merchants, the commodities are determined and designed in a commodity set mode and recommended to the merchants, so that new commodities which meet the user purchasing requirements better are designed for the merchants, the merchants can design commodities meeting the user requirements, and the sales rate of the merchants is improved.

In practical application, after determining the recommended commodity recommended to the merchant, in order to more intuitively reflect the effect that the recommended commodity can achieve after being issued, the recommended score of the new commodity can be displayed to the merchant through the recommended effect page of the new commodity shown in fig. 4, the higher the recommended score is, the better the new commodity popularization effect is, otherwise, the lower the recommended score is, the worse the new commodity popularization effect is, and a custom entry control exists in the page, the commodity can be customized through the control, so that preliminary knowledge on the issuing condition of the recommended commodity can be realized, and the merchant can conveniently make a judgment.

In specific implementation, the steps S104 to S108 may be implemented based on a commodity recommendation model; specifically, the input of the commodity recommendation model comprises the commodity elements, and the user data is called as input; and the recommended commodity can be output after the commodity recommendation model predicts.

In addition, after determining the recommended commodity, the merchant may generate commodity release information according to the recommended commodity so as to implement release processing on the recommended commodity, and may track a promotion effect of the recommended commodity in order to implement supervision on an actual sales effect of the recommended commodity.

In practical application, when the commodity data of the recommended commodity after being released is collected, the commodity data after being released can be collected according to a preset time node, namely, after the recommended commodity is released, the commodity data after being released is collected every 1 day, 3 days, 7 days and the like, so that the information of the promotion effect, the purchase rate and the like of the recommended commodity can be known according to the collected commodity data, and a merchant can more conveniently carry out promotion and supervision on the released recommended commodity; in addition, in order to facilitate the merchants to determine the promotion effect of the issued commodities more visually, the promotion effect distribution situation can be integrated through the commodity data, for example, a page fed back to the merchants is shown in fig. 5, the merchants can know the sales situation and the promotion effect of the commodities more visually through the promotion effect distribution shown in fig. 5, and the merchants can adjust the promotion effect more conveniently.

According to the commodity recommendation method, after the commodity elements submitted by the merchants for the target commodities are obtained, the target user groups corresponding to the commodity elements are screened out according to the user groups corresponding to the user data obtained in advance, then the commodity data corresponding to the target user groups are integrated to obtain the commodity preference distribution of the target user groups, and finally, based on the commodity preference distribution and the commodity elements, the recommended commodities are determined in the commodity set to which the target commodities belong for recommendation, the recommended commodities can be determined quickly and accurately based on the demands of the merchants, so that the merchants can trigger more users through the recommended commodities, the consumption cost of determining the recommended commodities by the merchants is reduced, and the experience of the merchants is improved to a great extent.

The embodiment of the commodity recommending device provided by the application is as follows:

corresponding to the above method embodiment, the present application further provides an embodiment of a commodity recommendation device, and fig. 6 shows a schematic structural diagram of a commodity recommendation device provided in an embodiment of the present application. As shown in fig. 6, the apparatus includes:

an obtaining module 602 configured to obtain a commodity element submitted by a merchant for a target commodity;

the screening module 604 is configured to screen a target user group corresponding to the commodity element from the user group according to a user group corresponding to pre-acquired user data;

an integration module 606 configured to integrate the commodity data of the target user group in the user data to obtain a commodity preference distribution of the target user group;

a determining module 608 configured to determine and recommend a recommended commodity in the commodity set to which the target commodity belongs based on the commodity preference distribution and the commodity elements.

In an optional embodiment, the commodity element is submitted by the merchant by triggering an element identifier displayed by the commodity element in a trigger display interface in a pre-configured commodity element library;

In an optional embodiment, the triggering display interface displays a custom element control, and if the custom element control is triggered, the element similarity between a custom commodity element and a commodity element in the commodity element library is calculated, and whether a commodity element with the element similarity to the custom commodity element larger than a preset similarity threshold exists in the commodity element library is judged;

In an optional embodiment, if the determination result after the step of determining whether there is a commodity element in the commodity element library whose element similarity with the user-defined commodity element is greater than the preset similarity threshold is non-existent, the following operations are performed:

In an optional embodiment, the screening module 604 includes:

a focus matching degree calculation unit configured to calculate a focus matching degree of the user in the user group and the target commodity according to a commodity brand of a merchant of the target commodity, the commodity element, and commodity data of the user in the user group included in the user data;

and the selecting user unit is configured to select users with attention matching degrees higher than a preset matching degree threshold value from the user group as target users to form the target user group.

In an alternative embodiment, the integration module 606 includes:

the data integration unit is configured to integrate attention data contained in the commodity data of the target user group to obtain commodity elements of which the attention degrees of the target user group meet preset conditions;

and the calculation unit is configured to calculate the attention number and the attention proportion of the target user group to each commodity element obtained through integration as the commodity preference distribution of the target user group.

In an alternative embodiment, the screening module 604, the integrating module 606, and the determining module 608 are implemented based on a product recommendation model; the input of the commodity recommendation model comprises the commodity elements, and the user data is called as input; and outputting the recommended commodity.

In an optional embodiment, the user data includes at least one of:

The commodity recommending device screens out the target user group corresponding to the commodity elements according to the user group corresponding to the user data acquired in advance after acquiring the commodity elements submitted by the merchant for the target commodities, and then integrates the commodity data corresponding to the target user group to obtain the commodity preference distribution of the target user group, and finally, based on the commodity preference distribution and the commodity elements, the commodities to which the target commodities belong are intensively determined to recommend the commodities, so that the determination of the recommended commodities based on the merchant demand can be fast and accurately realized, the merchant can trigger more users through the recommended commodities, the consumption cost of determining the recommended commodities by the merchant is reduced, and the experience of the merchant is greatly improved.

The above is a schematic solution of the commodity recommending apparatus of the present embodiment. It should be noted that the technical solution of the product recommendation device and the technical solution of the product recommendation method belong to the same concept, and details that are not described in detail in the technical solution of the product recommendation device can be referred to the description of the technical solution of the product recommendation method.

The embodiment of the commodity release processing method provided by the application is as follows:

fig. 7 is a flowchart illustrating a method for processing a product release according to an embodiment of the present application, which specifically includes the following steps:

step S702, a commodity issuing request submitted by a merchant is received.

The commodity issuing processing method provided by the application can reduce the cost of a merchant and simultaneously realize the rapid and accurate determination of commodity issuing information so as to enable the merchant to reach more users, and the commodity issuing processing method comprises the steps of reading commodity elements in a commodity element library for display based on the commodity issuing request after receiving the commodity issuing request submitted by the merchant, determining the commodity preference distribution of the target commodity elements in a user group based on the target commodity elements selected by the merchant, and finally generating and returning the commodity issuing information of the merchant to the merchant based on the commodity preference distribution and the target commodity elements, thereby realizing the rapid and accurate determination of the commodity issuing information according to the commodity issuing request of the merchant, enabling the merchant to more effectively reach the users according to the commodity issuing information, reducing the cost of the merchant for determining the commodity issuing information and shortening the determination period of the commodity issuing information, the experience of the merchant is improved to a great extent.

In specific implementation, the merchant specifically refers to a producer or a seller providing goods, and correspondingly, the goods release request specifically refers to a request submitted by the merchant for a goods release demand, and the related goods may be unpublished goods, and the determination of the goods release information at this time can realize improvement of the sales rate of the goods, or the goods may be already released goods, and the determination of the goods release information at this time can adjust the release policy of the goods, and may also be unproductive goods, and at this time, the determination of the goods release information can analyze goods of which the user is interested, so that goods meeting the user demand can be produced.

In practical application, when a commodity issuing request submitted by a merchant is received, the merchant is indicated to have an intention for determining user demands, wherein the intention is shown in that the merchant may need to issue a new commodity, or that the merchant is dissatisfied with the sales condition of a commodity currently sold, or that the merchant needs to produce a commodity similar to the user demands; based on the above, the corresponding commodity issuing information is generated according to the commodity issuing request of the merchant, so that the merchant can obtain the commodity issuing information with better user effect, and the merchant can issue the commodities which meet the user requirements better.

Step S704, based on the commodity issuing request, reading the commodity elements in the commodity element library and displaying the commodity elements to the merchant.

Specifically, on the basis of determining the commodity issuing request of the merchant, further, according to the commodity issuing request, reading the commodity element items in the element library for displaying by the merchant, where the commodity element specifically refers to attribute information of the target commodity in each attribute dimension, and for example, a color, a material, or a shape of the commodity may be referred to as a commodity element of the commodity.

Step S706, receiving the target commodity element selected by the merchant in the displayed commodity elements.

Specifically, on the basis of the above displaying of the commodity element to the merchant, further, when it is received that the merchant selects a target commodity element for the commodity element, it indicates that the merchant is about to release the commodity to be released, or the commodity which is not produced, or the released commodity, and the corresponding commodity element is the target commodity element.

Further, in order to accurately generate the commodity release information subsequently, the target commodity element needs to be accurately acquired, and the commodity element needs to be described by a merchant according to different requirements of different merchants, so that the commodity release information can be accurately generated subsequently, and in order to further improve the experience of the merchant and facilitate the operation of the merchant, in this embodiment, the commodity element can be displayed to the merchant through a triggering display interface, and the merchant can submit the target commodity element through an element identifier displayed by the triggering display interface; and the element identifiers of the commodity elements in the commodity element library are displayed on the triggering display interface according to the element grades to which the commodity elements belong.

Specifically, the commodity element library comprises a large number of commodity elements, and the commodity elements are displayed in a manner of element identification on a triggering display interface for a merchant to select; the element identifier for triggering the display interface to display specifically refers to a page that can be viewed by a merchant, and can display the display identifier corresponding to each basic commodity element in the commodity element library to the merchant, so that the merchant can determine the target commodity element by checking or clicking each element identifier, and it needs to be noted that all commodity elements corresponding to element identifiers submitted by the merchant are the target commodity elements.

Moreover, because the number of basic commodity elements contained in the commodity element library is large, if the element identifiers corresponding to all the basic commodity elements are displayed at one time through the trigger display interface, the checking or clicking by a merchant may not be convenient, and because the number is too large, the query of the target commodity element is also difficult, the commodity elements in the commodity element library can be divided according to the levels through the relationship of level mapping, the first level can be a category item, the second level is an attribute item under the category item, and the third level is an attribute value item under the attribute item; the method comprises the steps that a type item can exist before the type item, after a merchant selects the type item, all the type items under the type item are displayed through triggering a page corresponding to a display interface, after the type item is selected again, all attribute items under the type item are displayed, after the attribute item is selected again, all attribute values under the attribute item are displayed, finally, target commodity elements can be integrated according to the selected attribute values of the merchant, and in addition, a plurality of selectable items in any hierarchy can be selected simultaneously.

For example, in the case that the merchant is a merchant selling men's clothing, at this time, the merchant selects men's clothing items in the display page, sequentially displays all attribute items under the men's clothing items, the merchant selects "color item", "fabric item", and "trend element item", and respectively selects "electro-optic blue", "frosted", and "asymmetric" under each attribute item, it is determined that target commodity elements of the merchant are respectively "men's clothing", "electro-optic blue", "frosted", and "asymmetric", so as to be used for subsequently generating commodity publishing information.

Through providing the trigger display interface for the merchant, the target commodity elements of the merchant can be accurately determined while the merchant is convenient to operate, so that the accuracy of commodity issuing information generation can be improved, and the merchant can effectively reach more users.

if so, taking the commodity element with the highest element similarity with the user-defined commodity element as the target commodity element selected by the merchant;

if not, no processing is carried out.

Specifically, the custom element control displayed by the trigger display interface is an interface for facilitating a merchant to perform custom description on a target commodity element, if the merchant does not find the target commodity element in the trigger display interface, the custom commodity element can be uploaded by triggering the custom element control, and at this time, the element similarity between the custom commodity element and the commodity element in the commodity element library needs to be calculated, so that whether the custom commodity element exists in the commodity element library or not is detected;

based on this, the specific detection means is a mode of judging whether the element similarity is greater than a preset similarity threshold, if so, indicating that the commodity element corresponding to the customized commodity element exists in the commodity element library, the commodity element with the highest element similarity to the customized commodity element is taken as the target commodity element, and if not, indicating that the commodity element corresponding to the customized commodity element does not exist in the commodity element library, at this time, the customized commodity element can be taken as the target commodity element, and whether the customized commodity element meets the adding condition of adding into the commodity element library or not is also required to be detected; if yes, adding the self-defined commodity element into the commodity element library; if not, no processing is carried out.

According to the above example, when a men's clothing merchant selects commodity elements through a commodity element selection page, the fact that no men's clothing type attribute item exists in the page is found, the merchant selects a self-defining element control to enter the self-defining commodity elements, the entered self-defining commodity elements are 'hat shirts', the element similarity between the 'hat shirts' and each commodity element in a commodity element library is calculated, the highest similarity between the 'hat shirts' and the commodity elements is found to be 10%, and the similarity is smaller than a preset similarity threshold value, the fact that no 'hat shirts' commodity element exists in the commodity element library is shown, the 'hat shirts' are determined to be target commodity elements, and the commodity element library is expanded. The expansion of the commodity element library needs to detect whether the 'hat shirt' meets the condition of adding the commodity element library, and under the condition of meeting the condition, the 'hat shirt' is added into the commodity element library.

The target commodity elements are more convenient for the merchant to submit through the custom element control, and after the custom commodity elements are obtained, whether the target commodity elements can be added into the commodity element library or not is detected through a further judgment mode, so that the purpose of dynamically expanding the commodity element library is achieved, and high selectivity can be achieved when the merchant selects the commodity elements.

Step S708, determining the distribution of the commodity preference of the sub-user group corresponding to the target commodity element in the user group.

Specifically, on the basis of receiving the target commodity element submitted by the merchant, the commodity preference distribution of the sub-user groups corresponding to the target commodity element in the user group is further determined, so that the commodity release information can be generated more conveniently in the follow-up process, and more users can be reached.

The user group is determined according to pre-acquired user data, the user group is specifically a group formed by users corresponding to the acquired user data, and correspondingly, the sub-user group is specifically a group formed by users corresponding to the commodity elements screened from the user group.

Further, in the process of determining the distribution of the preference of the product, in order to generate the product release information more accurately, it is only possible to determine the distribution of the preference of the product accurately, and in this embodiment, the process of determining the distribution of the preference of the product is as follows:

In the process of screening the sub-user group, the accuracy of screening the sub-user group will affect the subsequent generation of the commodity release information, and after the commodity release information is determined, the number of reachable users also has a certain relation with the sub-user group, so that the sub-user group is accurately screened, and the sales rate of the merchant after releasing the commodity can be effectively improved, in this embodiment, the process of specifically screening the sub-user group is as follows:

Specifically, on the basis of the above-mentioned target commodity element acquisition, further, firstly, a commodity brand of the merchant, and commodity data of the user in the user group corresponding to the commodity element and the user data are determined; secondly, calculating the attention matching degree of each user in the user group and the target commodity based on the commodity brand, the commodity elements and the commodity data; and finally, selecting users higher than a preset matching degree threshold value from the calculated attention matching degrees as target users to form the target user group.

According to the above example, the target commodity element is determined to be { men's clothing, electro-optic blue, frosted sand, asymmetry and hats }, the commodity brand of a men's clothing merchant is a brand A, and the commodity data of each user in the user group is determined to comprise click data and browsing data based on the user data of a transaction platform for generating commodity release information;

at this time, the attention matching degree of each user in the user group is calculated based on the target commodity element, the brand A and the commodity data, the preset matching degree threshold value is 70%, the attention matching degree obtained through calculation is compared with the preset matching degree threshold value of 70%, it is determined that 10 general users exist in the user group and meet the condition, the 10 general users are determined as sub-users, the sub-user group is formed, and the sub-user group is used for subsequent commodity preference distribution based on the sub-user group, and commodity release information is accurately generated.

By combining the commodity data, the commodity brands and the commodity elements, the attention matching degree of each user in the sub-user group is calculated, and then the sub-user group is determined in a mode of comparing with a preset matching degree threshold value, so that the sub-user group can be accurately determined in the user group, the accuracy of commodity release information generated subsequently is promoted, the generated commodity release information is more consistent with the sub-user group, more users can be touched after a merchant releases commodities, and the sales rate of the merchant is improved.

Furthermore, in the process of obtaining the distribution of the commodity preferences of the sub-user group, in order to accurately obtain the distribution of the commodity preferences, so that the preferences of the sub-user group are reflected by the distribution conditions, and accurate generation of the commodity release information is realized, in this embodiment, the distribution of the commodity preferences is specifically determined in the following manner:

Specifically, on the basis of determining the commodity data corresponding to each user in the sub-user group, further determining the attention data corresponding to the target user group based on the commodity data, where the commodity data may refer to the corresponding description above, and this embodiment is not described in detail herein;

based on this, the attention data specifically refers to data related to commodities to which each sub-user pays attention in the sub-user group, the attention data may be data corresponding to types of commodities to which each sub-user likes, data corresponding to shapes of favorite commodities or data corresponding to colors of favorite commodities, and the like, and after the attention data is determined, the attention data is integrated to obtain commodity elements of which the attention degrees of the sub-user group meet preset conditions, and the obtained commodity elements specifically refer to commodity elements of which the preference of the sub-user group is higher, which are sorted out through the attention data of the sub-users in the sub-user group; and then, calculating attention figures and attention proportions corresponding to the commodity elements obtained after integration, so as to analyze the commodity preference distribution of the sub-user group.

In practical application, in order to facilitate the merchant to accurately grasp the preference of the sub-user group in the process of determining the distribution of the commodity preference, the merchant can be presented to the merchant through a presentation page after the distribution of the commodity preference corresponding to the sub-user group is generated, so that the merchant can better grasp the preference of the sub-user group; the content displayed to the merchant can be displayed by a bar chart, a fan-shaped chart, a broken line chart and a column chart, and in addition, the commodity preference distribution can be expressed in a pictogram mode, so that the merchant can more intuitively know the preference of the sub-user group.

According to the above example, after a sub-user group consisting of 10 ten thousand users is determined, according to commodity data (click data and browse data) of each user in the sub-user group, attention data of each user is determined, through integration of the attention data, commodity elements with the attention degrees meeting preset conditions of the sub-user group are obtained as { men's clothing, hat clothing, electro-optic blue and asymmetry }, then the attention number and the attention proportion of each commodity element are calculated, the attention number of men's clothing is determined to be 9.8 ten thousand users, and the attention proportion is 98%; the attention number of the cap blouse is 5 ten thousand users, and the attention proportion is 50%; the attention rate of the electro-optic blue is 1.2 ten thousand users, the attention rate is 12 percent, the attention rate of the asymmetric blue is 0.4 ten thousand users, and the attention rate is 4 percent; based on the determined attention number and attention proportion of each commodity element, commodity preference distribution of the target user group is generated according to the bar-shaped statistical chart, the fan-shaped statistical chart, the broken line statistical chart and the column statistical chart, and the commodity preference distribution is displayed to a men's clothing merchant through a report page so that the merchant can master preference distribution conditions of the users.

In addition, in the process of obtaining the preference distribution of the commodities corresponding to the sub-user group, besides determining the commodity elements whose attention degrees satisfy the preset conditions according to the attention data included in the commodity data, the commodity elements whose attention degrees satisfy the preset conditions may be determined according to click data, sales data or browsing data included in the commodity data, and a specific implementation manner is similar to the description content of the commodity elements whose attention degrees satisfy the preset conditions determined according to the attention data in this embodiment, which is not described in detail herein.

Step S710, based on the commodity preference distribution and the target commodity element, generating commodity release information of the merchant and sending the commodity release information to the merchant.

Specifically, on the basis of determining the commodity preference distribution corresponding to the sub-user group, the commodity release information is further generated by combining the commodity preference distribution and the target commodity element and is sent to the merchant, so that the merchant can reach more users after releasing commodities according to the commodity release information.

In this embodiment, the commodity release information includes at least one of the following: and updating element information according to the commodity preference distribution and the commodity information determined by the target commodity element based on the commodity preference distribution and the brand information of the joint brand determined by the target commodity element.

Further, in the first aspect, when the commodity release information is the new commodity information, it indicates that the merchant has not produced a new commodity, and at this time, the merchant may generate the new commodity information by the above-mentioned manner of generating the commodity release information, and then produce a new commodity according to the new commodity information, so that more users can be reached, and a commodity more meeting the user requirements can be provided.

According to the above example, under the condition that a men's clothing merchant is difficult to produce new men's clothing, the new commodity information determination service provided by the trading platform is used for determining that under the condition that commodity elements are { men's clothing, electro-optic blue, frosted sand, asymmetric hat shirt }, more users can be attracted, a sub-user group consisting of 10 thousands of users integrates commodity data of each user, a report corresponding to commodity preference distribution of the sub-user group is determined and displayed to the merchant, the merchant analyzes the new men's clothing with the hat shirt type, the electro-optic blue color and the asymmetric clothing pattern through the report to be popular, and at the moment, the new men's clothing can be produced according to the report to be published, so that the purchase rate of the users is improved, and the new men's clothing better meets the purchase requirements of the users.

In a second aspect, when the merchant has produced the commodity but has not issued the commodity, the merchant can generate the brand information by the above-mentioned method of generating the commodity issuing information, and then reach more users according to the brand information, and can provide the commodity more meeting the user's demand.

According to the above example, under the condition that a men's clothing merchant does not release new men's clothing, in order to improve the popularization effect and the purchase rate of the new men's clothing, the commodity release information is determined to be brand information through the commodity release information generating service provided by the transaction platform, namely the joint name release of the new men's clothing and the joint name brand can generate a good popularization effect, the joint name brand is determined through the commodity elements { men's clothing, electro-optic blue, frosted sand, asymmetry and hats }, and the report corresponding to the commodity preference distribution of the sub-user group, and the joint name release is performed on the basis of the joint name brand and the new men's clothing, so that the popularization effect of the new men's clothing is improved, and meanwhile, the purchase rate is improved.

In a third aspect, under the condition that the broad effect of the commodity issued by the merchant is not good, the merchant can generate the commodity updating element information by the above-mentioned mode of determining the commodity issuing information, and then more users can be reached according to the commodity updating element information, and the commodity more meeting the user requirements can be provided.

Along the above example, under the condition that the sales rate of men's clothing released by men's clothing merchants is not high, in order to improve the sales rate, the commodity release information is determined to be commodity update element information through the commodity release information generating service provided by the transaction platform, namely, the released men's clothing is updated, new commodity elements are blended in or old commodity elements are deleted, the commodity update elements are determined to be { hat shirt and electro-optic blue } through the commodity elements { men's clothing, electro-optic blue, frosted sand, asymmetry and hat shirt } and the report corresponding to the commodity preference distribution of the sub-user group, the released men's clothing is updated based on the commodity update elements, the updated men's clothing is generated and released, and the purchase rate of the commodities can be effectively improved.

In addition, after the commodity issuing information is sent to the merchant, in order to facilitate the merchant to know the sales condition of the target commodity after issuing the target commodity based on the commodity issuing information, in this embodiment, the commodity data of the target commodity is collected, and the specific implementation manner is as follows:

collecting commodity data of a target commodity issued by the merchant;

In practical application, after a merchant issues a target commodity according to the commodity issuing information, the commodity data of the target commodity is acquired at a certain time interval, wherein the commodity data specifically refers to click data of the commodity, sales data of the commodity, browsing data of the commodity and the like, so that the merchant can conveniently and visually know the issuing condition of the commodity, the sales effect of the commodity is analyzed, the merchant can conveniently monitor the target commodity, and the strategy can be timely adjusted.

According to the commodity issuing processing method provided by the embodiment, after a commodity issuing request submitted by a merchant is received, commodity elements in a commodity element library are read for display based on the commodity issuing request, commodity preference distribution of the target commodity elements in a user group is determined based on the target commodity elements selected by the merchant, and finally commodity issuing information of the merchant is generated and transmitted back to the merchant based on the commodity preference distribution and the target commodity elements.

Fig. 8 shows a processing flow chart applied to a commodity release scenario according to an embodiment of the present application, which specifically includes the following steps:

step S802, receiving a first commodity element submitted by a merchant for a commodity to be issued through a commodity element selection page.

In practical application, a merchant produces new clothes to be released, and in order to improve the sales rate of the clothes after the clothes are released, the merchant determines commodity release information of the new clothes through service provided by a transaction platform;

based on this, the merchant submits a first commodity element through the commodity element page, wherein the first commodity element corresponds to the clothes, and the first commodity element comprises { short sleeve, wine red, pure cotton }.

Step S804, the commodity data of the users in the user group corresponding to the user data is extracted.

Specifically, the user data specifically refers to a general term of data of registered users of the trading platform in various aspects, and correspondingly, the commodity data specifically refers to click data, sales data, browsing data and the like of each user in the user group.

Step S806, calculating the attention matching degree of the users and the commodities to be released in the user group according to the commodity brands, the first commodity elements and the commodity data of the commodities to be released.

Step S808, selecting users with the attention matching degree greater than a preset matching degree threshold from the user group to form a target user group.

Step S810, integrating the attention data included in the commodity data of the target user group, and obtaining a second commodity element whose attention degree of the target user group satisfies a preset condition.

Specifically, after a target user group corresponding to a new garment is screened out, second commodity elements are obtained by integrating concern data included in commodity data of the target user group, wherein the concern data specifically refers to commodity data and the like concerned by each target user, the second commodity elements specifically refer to commodity elements which are obtained based on the concern data of the target user group and accord with the preference of the target user group, and the second commodity elements include { patterns, no buttons }.

In step S812, the attention number and the attention proportion of the second commodity element are calculated based on the target user group as the commodity preference distribution of the target user group.

Specifically, the number of people concerned with the pattern in the second commodity element is determined to be 5 ten thousand users, the attention degree is 50%, the number of people concerned with no buttons is 1 ten thousand users, the attention degree is 10%, and the commodity preference distribution of the target user group is generated based on the number of people concerned with the pattern and the attention degree.

Step S814, determining a joint brand based on the distribution of commodity preferences and the first commodity element.

Specifically, after the commodity preference distribution is determined, the first commodity element { short sleeve, wine red and pure cotton } of the new clothes is combined, the new clothes and the brand B are determined to be jointly named and released to generate a good selling effect, and then the joint brand is determined based on the commodity preference distribution and the first commodity element.

And step S816, carrying out commodity joint name on the commodity to be issued and the joint name brand and issuing.

Specifically, after the new clothes and the brand B are determined to be linked, a good selling effect is achieved, at the moment, the new clothes and the brand B are subjected to commodity linking, and the new clothes after the linking are issued.

Step S818, collecting the sales data after the commodity to be issued is issued according to the preset time node.

And step S820, analyzing the sales data and determining the sales result of the issued commodity.

Specifically, under the condition that the new clothes and the brand second name are issued, in order to inform a merchant of the sales condition of the issued new clothes, the sales data of the issued commodity are collected according to a preset time node, and the sales data are analyzed to determine the sales result of the issued commodity; in addition, a sales distribution diagram can be integrated through the sales results and displayed to the merchant, so that the merchant can supervise the sales condition of the new clothes.

In conclusion, the commodity joint name strategy can be determined quickly and accurately based on the requirements of the merchant, so that the merchant can more effectively reach more users after issuing the new commodities, the cost of determining the commodity joint name strategy by the merchant is reduced, the experience of the merchant is improved to a great extent, and meanwhile, the sales data can be collected according to the preset time node after issuing the new commodities, so that the sales condition of the new commodities is reflected, the sales data of the new commodities can be known without research by the merchant, and the merchant can conveniently supervise the sales data.

The embodiment of a commodity release processing device provided by the application is as follows:

corresponding to the above method embodiment, the present application further provides an embodiment of a product release processing apparatus, and fig. 9 shows a schematic structural diagram of a product release processing apparatus provided in an embodiment of the present application. As shown in fig. 9, the apparatus includes:

a receiving module 902 configured to receive a commodity issuing request submitted by a merchant;

a reading module 904 configured to read the commodity elements in the commodity element library and display the commodity elements to the merchant based on the commodity issuing request;

a selection module 906 configured to receive a target merchandise element selected by the merchant among the merchandise elements displayed;

a determining module 908 configured to determine a distribution of commodity preference of a sub-user group corresponding to the target commodity element in the user group; the user group is determined according to user data acquired in advance;

a generating module 910 configured to generate and send commodity issuing information of the merchant to the merchant based on the commodity preference distribution and the target commodity element.

In an optional embodiment, the determining module 908 includes:

a screening sub-user group unit configured to screen a sub-user group corresponding to the target commodity element from the user group;

and the integrated commodity data unit is configured to integrate the commodity data of the sub-user group in the user data to obtain the commodity preference distribution of the sub-user group.

In an optional embodiment, the filtering the sub-user group unit includes:

a matching degree calculating subunit configured to calculate a degree of attention matching between the user in the user group and the target commodity element according to the commodity brand of the merchant, the commodity element, and commodity data of the user in the user group included in the user data;

and the selecting subunit is configured to select users with attention matching degrees higher than a preset matching degree threshold value from the user group to form the sub-user group.

In an optional embodiment, the integrated merchandise data unit includes:

the integration subunit is configured to integrate the attention data included in the commodity data of the target user group to obtain a commodity element of which the attention degree of the target user group meets a preset condition;

and the calculation subunit is configured to calculate the attention number and the attention proportion of the target user group to each commodity element obtained through integration as the commodity preference distribution of the target user group.

In an optional embodiment, the commodity element is displayed to the merchant through a triggering display interface, and the merchant submits the target commodity element through an element identifier displayed through triggering the triggering display interface;

In an optional embodiment, the commodity release information includes at least one of the following:

In an optional embodiment, the article release processing apparatus further includes:

the acquisition module is configured to acquire commodity data of a target commodity issued by the merchant;

In an optional embodiment, the user data includes at least one of:

The commodity issuing processing apparatus that this embodiment provided, after receiving the commodity issuing request that the trade company submitted, the commodity element in the commodity element storehouse is read and is demonstrateed based on the commodity issuing request, target commodity element based on the trade company selection, confirm the commodity preference distribution of target commodity element in the user group, at last based on commodity preference distribution and target commodity element, the commodity issuing information of production trade company is given the trade company in the passback, the quick accurate commodity issuing information of confirming according to the commodity issuing request of trade company has been realized, make the trade company can reach the user according to the more effective touch of commodity issuing information, the cost of the commodity issuing information of trade company's definite goods has not only been reduced, the definite period of commodity issuing information has still been shortened, to a great extent has improved the experience of trade company.

Fig. 10 illustrates a block diagram of a first computing device 1000 provided according to an embodiment of the present application. The components of the computing device 1000 include, but are not limited to, memory 1010 and a processor 1020. The processor 1020 is coupled to the memory 1010 via a bus 1030 and the database 1050 is used to store data.

Computing device 1000 also includes access device 1040, access device 1040 enabling computing device 1000 to communicate via one or more networks 1060. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. Access device 1040 may include one or more of any type of network interface, e.g., a Network Interface Card (NIC), wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.

In one embodiment of the present application, the above-described components of computing device 1000 and other components not shown in FIG. 10 may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 10 is for purposes of example only and is not limiting as to the scope of the present application. Those skilled in the art may add or replace other components as desired.

Computing device 1000 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), a mobile phone (e.g., smartphone), a wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 1000 may also be a mobile or stationary server.

Wherein, the processor 1020 is configured to execute the following computer-executable instructions:

The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the technical solution of the above-mentioned product recommendation method belong to the same concept, and details that are not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the above-mentioned product recommendation method.

Fig. 11 illustrates a block diagram of a second computing device 1100 provided in accordance with an embodiment of the present application. The components of the computing device 1100 include, but are not limited to, memory 1110 and a processor 1120. The processor 1120 is coupled to the memory 1110 via a bus 1130 and the database 1150 is used to store data.

The computing device 1100 also includes an access device 1140, the access device 1140 enabling the computing device 1100 to communicate via one or more networks 1160. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. The access device 1140 may include one or more of any type of network interface, e.g., a Network Interface Card (NIC), wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.

In one embodiment of the application, the above-described components of computing device 1100, as well as other components not shown in FIG. 11, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 11 is for purposes of example only and is not limiting as to the scope of the present application. Those skilled in the art may add or replace other components as desired.

Computing device 1100 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smartphone), wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 1100 can also be a mobile or stationary server.

Wherein, the processor 1120 is configured to execute the following computer-executable instructions:

receiving a commodity issuing request submitted by a merchant;

The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the technical solution of the above-mentioned commodity issuing processing method belong to the same concept, and details that are not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the above-mentioned commodity issuing processing method.

An embodiment of the present application further provides a first computer-readable storage medium storing computer instructions that, when executed by a processor, are configured to:

The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the above commodity recommendation method, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the above commodity recommendation method.

An embodiment of the present application further provides a second computer-readable storage medium storing computer instructions that, when executed by a processor, are configured to:

receiving a commodity issuing request submitted by a merchant;

The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the above-mentioned commodity distribution processing method, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the above-mentioned commodity distribution processing method.

The foregoing description of specific embodiments of the present application has been presented. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The preferred embodiments of the present application disclosed above are intended only to aid in the explanation of the application. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and its practical applications, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A method of merchandise recommendation, comprising:

2. The merchandise recommendation method according to claim 1, wherein the merchandise elements are submitted by the merchant by triggering element identifiers displayed by the merchandise elements in a pre-configured merchandise element library on a triggering display interface;

3. The commodity recommendation method according to claim 2, wherein a custom element control is displayed on the trigger display interface, if the custom element control is triggered, the element similarity between a custom commodity element and a commodity element in the commodity element library is calculated, and whether a commodity element with the element similarity between the custom commodity element and the commodity element in the commodity element library being greater than a preset similarity threshold exists is judged;

4. The commodity recommendation method according to claim 3, wherein if the commodity element library does not contain a commodity element whose element similarity with the user-defined commodity element is greater than a preset similarity threshold, the following operation is performed:

5. The commodity recommendation method according to claim 1, wherein the screening, according to a user group corresponding to pre-obtained user data, a target user group corresponding to the commodity element from the user group comprises:

6. The commodity recommendation method according to claim 1, wherein the integrating commodity data of the target user group in the user data to obtain a commodity preference distribution of the target user group comprises:

7. The commodity recommendation method according to claim 1, wherein the step of screening a target user group corresponding to the commodity element from the user group according to a user group corresponding to pre-obtained user data, the step of integrating commodity data of the target user group from the user data to obtain a commodity preference distribution of the target user group, and the step of determining and recommending a recommended commodity in a commodity set to which the target commodity belongs based on the commodity preference distribution and the commodity element are realized based on a commodity recommendation model;

and outputting the recommended commodity.

8. The article recommendation method of claim 1, the user data comprising at least one of:

9. An article recommendation device comprising:

10. A commodity release processing method comprises the following steps:

receiving a commodity issuing request submitted by a merchant;

11. The merchandise release processing method according to claim 10, wherein the determining of the merchandise preference distribution of the sub-user group corresponding to the target merchandise element in the user group comprises:

12. The merchandise release processing method according to claim 11, wherein the screening of the user group for the sub-user group corresponding to the target merchandise element includes:

13. The commodity release processing method according to claim 11, wherein the integrating the commodity data of the sub-user group in the user data to obtain the commodity preference distribution of the sub-user group includes:

14. The merchandise release processing method according to claim 10, wherein the merchandise element is displayed to the merchant through a triggering display interface, and the merchant submits the target merchandise element through an element identifier which triggers the triggering display interface to display;

15. The merchandise release processing method according to claim 14, wherein a custom element control is displayed on the trigger display interface, if the custom element control is triggered, the element similarity between a custom merchandise element and a merchandise element in the merchandise element library is calculated, and whether a merchandise element with the element similarity to the custom merchandise element larger than a preset similarity threshold exists in the merchandise element library is judged;

16. The method of claim 15, wherein if the step of determining whether or not there is a commodity element in the commodity element library whose element similarity with the user-defined commodity element is greater than a preset similarity threshold is performed, the following operation is performed:

17. The product distribution processing method according to claim 10, wherein the product distribution information includes at least one of:

18. The method of claim 17, wherein after the steps of generating and sending the merchandise distribution information of the merchant to the merchant based on the merchandise preference distribution and the target merchandise element are executed, the method further comprises:

collecting commodity data of a target commodity issued by the merchant;

19. The merchandise distribution processing method according to claim 10, wherein the user data includes at least one of:

20. An article issuance processing apparatus comprising:

21. A computing device, comprising:

a memory and a processor;

22. A computing device, comprising:

a memory and a processor;

receiving a commodity issuing request submitted by a merchant;

23. A computer-readable storage medium storing computer instructions which, when executed by a processor, carry out the steps of the method of recommending items of claim 1 to 8.

24. A computer-readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the method of processing an article release according to any of claims 10 to 19.