WO2019090619A1

WO2019090619A1 - Commodity recommending method and commodity recommending system based on intelligent terminal

Info

Publication number: WO2019090619A1
Application number: PCT/CN2017/110250
Authority: WO
Inventors: 詹昌松
Original assignee: 深圳传音通讯有限公司
Priority date: 2017-11-09
Filing date: 2017-11-09
Publication date: 2019-05-16
Also published as: CN111316306A

Abstract

The present invention provides a commodity recommending method and a commodity recommending system based on an intelligent terminal. The commodity recommending method the following steps: receiving the name of a commodity to be purchased; determining the category of the commodity to be purchased according to the name of the commodity to be purchased; matching, for the category of the commodity to be purchased, a corresponding preset commodity category in a preset catalog; and recommending a preset commodity list associated with the preset commodity category. The commodity recommending system comprises: a receiving module, a determining module, a matching module, and a recommending module. By objectively analyzing commodities on the basis of big data, the present invention provides an optimized purchasing solution for a user, and can not only ensure that the user can purchase high-quality products but also save purchase time for the user, thereby bringing better online purchase experience for the user.

Description

Product recommendation method based on intelligent terminal and product recommendation system

Technical field

The present invention relates to the field of control of intelligent terminals, and in particular, to a product recommendation method and a product recommendation system based on an intelligent terminal.

Background technique

At present, the rapid development of Internet technology has changed the current lifestyle of people, such as taxiing, paying, shopping, booking, and eating. Thanks to the Internet's connectivity, even if you don't leave your home, you can immediately purchase products from all over the world with a click of the mouse.

At present, in order to facilitate the selection of users, online shopping malls generally use different recommendation rules to recommend products for users, including: purchase quantity, quality, price, relevance and other different recommendation factors. However, based on the online mall, the order of the store or the goods can be improved through the purchase mechanism. Therefore, the purchase data of a single online mall often cannot objectively reflect the degree of good or bad of a certain product.

Therefore, it is necessary to provide a product recommendation method and a product recommendation system based on the smart terminal, which can distinguish the category of the product based on the big data of the user purchasing the product on different network platforms, and set a comprehensive sorting for each product category association. A list of products, based on big data to objectively analyze the products, to provide users with an optimized purchase plan, which can ensure that users can purchase high-quality products, and at the same time save users time to purchase, and bring a better online to users. Purchase experience.

Summary of the invention

In order to solve the above problems, the present invention provides a product recommendation method and a product recommendation system based on a smart terminal. Based on the big data that the user purchases the goods on different network platforms, by distinguishing the categories of the products, and setting a comprehensive sorted product list for each product category association, objectively analyzing the products based on the big data, providing an optimized user The purchase plan not only ensures that users can purchase high-quality products, but also saves users time to purchase, which brings users a better online shopping experience.

The invention provides a product recommendation method based on a smart terminal, and the product recommendation method comprises the following steps:

Receiving a name to purchase a product to purchase;

Determining the desired item to be purchased according to the name of the purchase to be purchased;

And matching, in a preset directory, a corresponding preset product category for the item to be purchased;

A list of preset items associated with the preset item category is recommended.

Preferably, the product recommendation method further comprises:

Recording information data of each user's purchased goods;

Classifying each of the purchased items to establish a preset directory including the preset item categories;

And according to the information data, a preset item list is associated with each of the preset item categories.

Preferably, the step of associating a predetermined item list with each of the preset item categories according to the information data further includes:

Reading the information data to obtain a ranking factor including the purchased name of the purchased item, the purchased quantity, the purchased item, the purchased price, and/or the purchased brand;

Sorting the purchased items under each of the purchased categories according to the sorting factor;

Saving the purchased item ranked as the top 10 in the same predetermined item as the purchased item The list of the preset items associated with the category.

Preferably, the product recommendation method further comprises:

Updating the preset item list within a preset time period.

Preferably, the step of sorting the purchased items under each of the purchased items according to the purchased name, the purchased quantity, the purchased price, and the purchased brand further includes:

Identifying a calculation condition instruction;

Sorting the purchased items under each of the purchased items according to the calculation condition instruction;

The calculating condition instruction includes increasing the sorting factor, reducing the sorting factor, or changing the sorting factor.

The present invention further provides a product recommendation system based on an intelligent terminal, the product recommendation system comprising: a receiving module, a determining module, a matching module, and a recommendation module;

The receiving module is in communication with the determining module, and receives a name to be purchased for purchasing an item;

The determining module is communicably connected to the receiving module and the matching module, and determines the item to be purchased according to the name of the purchase to be purchased;

The matching module is connected to the determining module and the recommendation module, and matches a predetermined preset product category for the desired product category in a preset directory;

The recommendation module is in communication with the matching module, and recommends a preset product list associated with the preset product category.

Preferably, the commodity recommendation system further comprises: a recording module, an establishing module and an associating module;

The recording module records information data of each user's purchased goods;

The establishing module is connected to the recording module and the matching module, and classifies each of the purchased products to establish a preset directory including the preset product category;

The association module is connected to the recording module, the establishing module, and the recommendation module, and associates a preset product list with each of the preset product categories according to the information data.

Preferably, the association module further comprises:

Reading unit, reading the information data to obtain a ranking factor including the purchased name of the purchased item, the purchased quantity, the purchased item, the purchased price, and/or the purchased brand;

a sorting unit, sorting the purchased items under each of the purchased categories according to the sorting factor;

The saving unit stores the purchased items sorted into the top 10 in the preset item list associated with the preset item category that is the same as the purchased item category.

Preferably, the commodity recommendation system further includes an update module;

The update module is in communication with the association module, and updates the preset item list within a preset time period.

Preferably, the sorting unit further comprises:

Identifying an element and identifying a calculation condition instruction;

Executing, according to the calculation condition instruction, sorting the purchased goods under each of the purchased categories;

Compared with the prior art, the technical advantages of the present invention are:

Based on the big data that the user purchases the goods on different network platforms, by distinguishing the categories of the products, and setting a comprehensive sorted product list for each product category association, objectively analyzing the products based on the big data, providing an optimized user The purchase plan not only ensures that users can purchase high-quality products, but also saves users time to purchase, which brings users a better online shopping experience.

DRAWINGS

1 is a schematic flow chart of a method for recommending a product based on an intelligent terminal according to an embodiment of the present invention;

2 is a schematic flow chart of a method for recommending a product based on an intelligent terminal according to an embodiment of the invention;

3 is a schematic structural diagram of a product recommendation system based on an intelligent terminal according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a product recommendation system based on an intelligent terminal according to an embodiment of the invention.

Detailed ways

The advantages of the present invention are explained in detail below with reference to the accompanying drawings and specific embodiments.

The following description refers to the same or similar elements in the different figures unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with aspects of the present disclosure as detailed in the appended claims.

Referring to FIG. 1 , it is a schematic flowchart of a method for recommending a product based on an intelligent terminal according to an embodiment of the present invention. As can be seen from the figure, in the embodiment, the product recommendation method includes the following steps:

- receiving a wish to purchase a product;

When a user needs to purchase a product on a certain network platform through the smart terminal, in order to obtain a more convenient and high-quality shopping experience, the user may first open the product recommendation program in the smart terminal to enter the product recommendation mode. When the smart terminal is in the item recommendation mode, the user can input the name of the item to be purchased in a search box, for example, input the name to be purchased as A.

- determining the desired item to be purchased according to the name of the purchase to be purchased;

After inputting the name to be purchased, the smart terminal extracts the field in the name to be purchased, and obtains the category to be purchased by the user to purchase the item by matching the field with the remote database. Among them, the category includes large and small categories. For example, if you want to buy the name "skirt", at this time, according to the matching of the fields, the smart terminal will obtain the general category of the product to be purchased, and the small category is skirt or women's clothing.

- matching, in a preset directory, a corresponding preset product category for the item to be purchased;

After judging the category of the user who wants to purchase the product, the smart terminal automatically retrieves a preset directory. In the preset directory, a predetermined product category is recorded. The intelligent terminal automatically matches the corresponding preset product category in the preset directory according to the judgment to obtain the commodity category, and preferably matches the commodity category to provide the user with more accurate product recommendation. For example, if a large category of goods to be purchased is a costume, and a small category is a skirt, the default product category corresponding to the "skirt" is preferentially matched. If there is no "dress" in the preset product category, the corresponding product category corresponding to "dress" is further matched.

- Recommend a list of preset items associated with the preset item category.

After the matching of the preset product category is completed for the product to be purchased by the user, the smart terminal automatically recommends to the user a preset product list set in association with the preset product category. The preset product list includes a product recommendation list based on the Internet user's online shopping mall to purchase big data, and provides an objective product recommendation and reference for the user to filter the products to be purchased.

Referring to FIG. 2, it is a schematic flowchart of a method for recommending a product based on an intelligent terminal according to an embodiment of the present invention. As shown in the figure, in a preferred embodiment, the product recommendation method further includes:

- recording information data of each user's purchased goods;

In order to objectively and accurately optimize the screening and recommendation for the user to purchase the product, the intelligent terminal records the information data of each item purchased through the Internet on the online mall platform through a third-party remote server. The information data may include: purchased platform, purchased items, categories, prices, origin, quality, quantity, brand, and the like. Detailed information data ensures the reliability and objectivity of product recommendations.

- classifying each of the purchased items to create a preset list containing the predetermined item categories;

Based on the big data of the products that the user has purchased, the smart terminal automatically classifies the purchased products by category, Create a preset catalog containing preset merchandise categories. In the preset catalogue, you can obtain data information of different merchandise under each category by consulting different preset merchandise categories, including the purchased platform, purchased items, categories, and prices. , origin, quality, quantity, brand, etc., so that smart terminals can quickly recommend products to users.

- associating a predetermined item list for each of the preset item categories based on the information data.

Based on the information data of the purchased goods obtained by the smart terminal through the big data, the smart terminal may sort different products under the same category according to a preset rule, and preferably sort the top products and add them to a preset product list. The preset product list is associated with the preset product category, so that once the smart terminal locks the preset product category according to the desired purchase field input by the user, the preset product list can be directly retrieved for the user's reference.

In a preferred embodiment, the step of associating a predetermined item list with each of the preset item categories according to the information data further includes:

After obtaining the information data that the user has purchased, the smart terminal automatically reads the information data, and extracts the main information such as the name, quantity, category, price, and/or brand of the purchased product from the information data, as the same product category. The evaluation factors for the ranking of different commodities.

- sorting the purchased items under each of the purchased categories according to the sorting factor;

According to the ranking factors of the purchased goods, such as the name, quantity, category, price and/or brand, the intelligent terminal will calculate the high-low recommendation of different commodities under the same product category based on the comprehensive calculation of each sorting factor. Sort.

- saving the purchased item sorted into the top 10 bits in the preset item list associated with the preset item category that is the same as the purchased item category.

After sorting between different commodities under the same product category, the smart terminal extracts the top 10 purchased products, adds them to the preset product list, and selects the preset product list and the preset product category. Correlation, in this way, when the smart terminal finds a matching preset product category according to the category to be purchased, it can automatically issue a preset product list to the user, recommend the product for the user based on the big data, and provide the user with the purchase suggestion and reference.

In a preferred embodiment, the item recommendation method further includes:

- updating the preset item list within a preset time period.

In order to ensure the accuracy of big data, in order to provide users with the most reliable product recommendation, the preset product list in the smart terminal will also be automatically updated within a preset time period, that is, the change of big data that continuously purchases goods through the Internet user. Update the sorting between different products under the same product category to get the latest preset product list to provide users with more optimized purchasing reference.

In a preferred embodiment, the step of sorting the purchased items under each of the purchased items according to the purchased name, the purchased quantity, the purchased price, and the purchased brand further includes:

- identifying a calculation condition instruction;

In order to ensure that the preset product list provided by the smart terminal is more suitable for the user's purchase demand, the user can further adjust the ranking factors of different commodities under the same product category by issuing a calculation condition instruction to the smart terminal.

- sorting the purchased items under each of the purchased items according to the calculation condition instruction;

According to the calculation condition instruction actually issued by the user to the intelligent terminal, the intelligent terminal further reorders the different commodities under the preset product category according to the calculation condition to obtain a preset product list that more closely matches the actual screening condition of the user. For example, if the user purchases the product and pays more attention to the price, the smart terminal can issue a calculation condition instruction about the price, and the ranking in the preset product list finally provided by the smart terminal is mainly based on the price of the previous user's purchased product. Sort the different items under the product category.

- wherein the calculating conditional instructions include increasing the ranking factor, decreasing the ranking factor, or changing the ranking factor.

The calculation condition instructions issued by the user are mainly for including the purchased name, the purchased quantity, the purchased price, and the Buy adjustments such as branding and other ranking factors. For example, when the user purchases the item only pays attention to the price, the preset item list recommended by the smart terminal may be the top 10 recommended items obtained only by sorting according to the purchase price.

Referring to FIG. 3, it is a schematic structural diagram of a product recommendation system based on an intelligent terminal according to an embodiment of the present invention. As shown in the figure, in the embodiment, the product recommendation system includes: a receiving module, a determining module, a matching module, and a recommendation module; and the receiving module is in communication with the determining module, and receives a product to be purchased. Want to buy a name;

When a user needs to purchase a product on a certain network platform through the smart terminal, in order to obtain a more convenient and high-quality shopping experience, the user may first start a product recommendation system based on the smart terminal to enter the product recommendation mode. When in the item recommendation mode, the user can input the name of the item to be purchased in a search box, for example, input the name to be purchased as A.

- the judging module is communicably connected to the receiving module and the matching module, and determining the item to be purchased according to the name of the purchase to be purchased;

After the receiving module receives the user input to purchase the name, the determining module in the product recommendation system extracts the field in the name to be purchased, and obtains the desired item to be purchased by the user by matching the field with the remote database. Among them, the category includes large and small categories. For example, if you want to buy the name "skirt", at this time, according to the matching of the fields, the smart terminal will obtain the general category of the product to be purchased, and the small category is skirt or women's clothing.

- the matching module is connected to the determining module and the recommendation module, and matches a predetermined preset product category for the desired product category in a preset directory;

After the judging module judges that the category of the product to be purchased by the user is obtained, the matching module automatically retrieves a preset directory. In the preset directory, a predetermined product category is recorded. The matching module automatically matches the preset product category corresponding to the corresponding product category in the preset directory according to the judgment module, and preferably matches the product category to provide the user with more accurate product recommendation. For example, if the judging module judges that the major category of the merchandise category to be purchased is clothing, and the subclass is the skirt, the matching module preferentially matches the preset merchandise category corresponding to the “skirt”. If the matching module determines that there is no "dress" in the preset product category, the matching product category corresponding to the "dress" is further matched.

After the matching module of the product recommendation system completes the matching of the preset product category for the product to be purchased by the user, the recommendation module automatically recommends to the user a preset product list set in association with the preset product category. The preset product list includes a product recommendation list based on the Internet user's online shopping mall to purchase big data, and provides an objective product recommendation and reference for the user to filter the products to be purchased.

Referring to FIG. 4, it is a schematic structural diagram of a product recommendation system based on an intelligent terminal according to an embodiment of the present invention. As shown in the figure, in a preferred embodiment, the item recommendation system further includes: a recording module, an establishing module, and an associating module; - the recording module, recording information data of each user's purchased goods;

In order to objectively and accurately optimize the screening and recommendation for the user to purchase the product, the product recommendation system further includes the recording module recording the information data of each item purchased through the Internet on the online mall platform through a third-party remote server. The information data may include: purchased platform, purchased items, categories, prices, origin, quality, quantity, brand, and the like. Detailed information data ensures the reliability and objectivity of product recommendations.

- the establishing module, in communication with the recording module and the matching module, classifying each of the purchased products to establish a preset directory including the preset product category;

Based on the big data of the user's purchased goods, the product recommendation system's building module automatically classifies the purchased products by category to establish a preset directory containing the preset product categories. In the preset directory, the different pre-views can be consulted. Set the commodity category to obtain data information of different commodities under each category, including the purchased platform, purchased items, categories, prices, origin, quality, quantity, brand, etc., so that the product recommendation system can quickly recommend products to users.

- the association module is in communication with the recording module, the establishing module, and the recommendation module, according to the information Data, associated with a predetermined item list for each of the predetermined item categories.

Based on the information data of the purchased product obtained by the recording module through the big data, the association module may sort different products under the same category according to a preset rule, and preferably sort the top products and add them to a preset product list. And the preset product list is associated with the preset product category, so that once the matching module locks the preset product category in the module according to the user to purchase the field, the recommendation module can directly retrieve the preset in the associated module. A list of products for your reference.

In a preferred embodiment, the association module further comprises: a reading unit that reads the information data to obtain a purchased name including the purchased item, a purchased quantity, a purchased item, and a purchased price. And/or sorting factors in the purchased brand;

After the recording module obtains the information data that the user has purchased, the reading unit in the association module automatically reads the information data, and extracts the main information such as the name, quantity, category, price, and/or brand of the purchased product from the information data. As an evaluation factor for ranking different commodities under the same product category.

a sorting unit that sorts the purchased items under each of the purchased categories according to the sorting factor;

According to the ranking factors such as the name, quantity, category, price and/or brand of the purchased goods read by the reading unit, the sorting unit in the associated module will be based on the comprehensive calculation of each sorting factor, and the same product category will be obtained. The order of recommendation of different commodities is from high to low.

a saving unit that stores the purchased items sorted into the top 10 bits in the preset item list associated with the preset item category that is the same as the purchased item category.

After the sorting unit sorts the different items under the same product category, the saving unit extracts the top 10 purchased items, adds them to the preset item list, and lists the preset items with the presets. The product category is associated, so that when the matching module finds a matching preset product category according to the desired product category, the recommendation module can automatically issue a preset product list to the user through the association module, and recommend the product to the user based on the big data, and provide the user with the product. Buy suggestions and references.

In a preferred embodiment, the item recommendation system further includes an update module; and the update module is in communication with the association module to update the preset item list within a preset time period.

In order to ensure the accuracy of big data, in order to provide users with the most reliable product recommendation, the product recommendation system further has an update module to ensure that the preset product list in the associated module is also automatically updated within a preset time period, ie The ordering between different commodities under the same product category is continuously updated through the change of big data purchased by Internet users to obtain the latest preset product list, so as to provide users with more optimized purchase reference.

In a preferred embodiment, the sorting unit further includes: further comprising:

Identifying the component and identifying a calculation condition instruction;

The sorting unit ensures that the preset product list provided by the sorting unit is more suitable for the user's purchase demand, and the built-in identification component can further identify the user issuing the calculation condition instruction, and further sort the different commodities under the same product category based on the calculation condition instruction. Factors are adjusted.

An execution component that sorts the purchased items under each of the purchased categories according to the calculation condition instruction;

According to the calculation condition instruction actually issued by the identification component, the execution component further sorts the different commodities under the preset commodity category according to the calculation condition to obtain a preset commodity list that more closely matches the actual screening condition of the user. For example, if the user purchases the product and pays more attention to the price, the smart terminal can issue a calculation condition instruction about the price, and the ranking in the preset product list finally provided by the smart terminal is mainly based on the price of the previous user's purchased product. Sort the different items under the product category.

The smart terminal-based product recommendation method and the product recommendation system provided by the present invention can distinguish the category of the product based on the big data of the user purchasing the product on different network platforms, and set a comprehensive sorted product list for each product category association. Based on the objective analysis of commodities based on big data, it provides users with an optimized purchase plan, which not only ensures users to purchase quality products, but also saves users time for purchase and brings a better online shopping experience for users. .

It should be noted that the embodiments of the present invention are preferred embodiments, and are not intended to limit the scope of the present invention. Any one skilled in the art may use the above-disclosed technical contents to change or modify the equivalent embodiments. Any modification or equivalent changes and modifications of the above embodiments in accordance with the technical spirit of the present invention are still within the scope of the technical solutions of the present invention.

Claims

A product recommendation method based on a smart terminal, characterized in that

The product recommendation method includes the following steps:

Receiving a name to purchase a product to purchase;

Determining the desired item to be purchased according to the name of the purchase to be purchased;

And matching, in a preset directory, a corresponding preset product category for the item to be purchased;

A list of preset items associated with the preset item category is recommended.
A product recommendation method according to claim 1, wherein

The product recommendation method further includes:

Recording information data of each user's purchased goods;

Classifying each of the purchased items to establish a preset directory including the preset item categories;

And according to the information data, a preset item list is associated with each of the preset item categories.
A product recommendation method according to claim 2, wherein

According to the information data, the step of associating a predetermined item list with each of the preset item categories further includes:

Reading the information data to obtain a ranking factor including the purchased name of the purchased item, the purchased quantity, the purchased item, the purchased price, and/or the purchased brand;

Sorting the purchased items under each of the purchased categories according to the sorting factor;

The purchased items sorted into the top 10 bits are stored in the preset item list associated with the preset item category that is the same as the purchased item category.
A product recommendation method according to claim 2 or 3, characterized in that

The product recommendation method further includes:

Updating the preset item list within a preset time period.
A product recommendation method according to claim 3, wherein

And the step of sorting the purchased products under each of the purchased categories according to the purchased name, the purchased quantity, the purchased price, and the purchased brand, further comprising:

Identifying a calculation condition instruction;

Sorting the purchased items under each of the purchased items according to the calculation condition instruction;

The calculating condition instruction includes increasing the sorting factor, reducing the sorting factor, or changing the sorting factor.
A product recommendation system based on a smart terminal, characterized in that

The commodity recommendation system includes: a receiving module, a determining module, a matching module, and a recommendation module;

The receiving module is in communication with the determining module, and receives a name to be purchased for purchasing an item;

The determining module is communicably connected to the receiving module and the matching module, and determines the item to be purchased according to the name of the purchase to be purchased;

The matching module is connected to the determining module and the recommendation module, and matches a predetermined preset product category for the desired product category in a preset directory;

The recommendation module is in communication with the matching module, and recommends a preset product list associated with the preset product category.
A merchandise recommendation system according to claim 6, wherein

The commodity recommendation system further includes: a recording module, an establishing module, and an associated module;

The recording module records information data of each user's purchased goods;

The establishing module is connected to the recording module and the matching module, and divides each purchased item into a class to create a preset directory containing the preset product category;

The association module is connected to the recording module, the establishing module, and the recommendation module, and associates a preset product list with each of the preset product categories according to the information data.
A merchandise recommendation system according to claim 6, wherein

The association module further includes:

Reading unit, reading the information data to obtain a ranking factor including the purchased name of the purchased item, the purchased quantity, the purchased item, the purchased price, and/or the purchased brand;

a sorting unit, sorting the purchased items under each of the purchased categories according to the sorting factor;

The saving unit stores the purchased items sorted into the top 10 in the preset item list associated with the preset item category that is the same as the purchased item category.
A product recommendation system according to claim 7 or 8, wherein

The item recommendation system further includes an update module;

The update module is in communication with the association module, and updates the preset item list within a preset time period.
A merchandise recommendation system according to claim 8, wherein

The sorting unit further includes:

Identifying an element and identifying a calculation condition instruction;

Executing, according to the calculation condition instruction, sorting the purchased goods under each of the purchased categories;

The calculating condition instruction includes increasing the sorting factor, reducing the sorting factor, or changing the sorting factor.