CN111654714B

CN111654714B - Information processing method, apparatus, electronic device and storage medium

Info

Publication number: CN111654714B
Application number: CN202010450387.6A
Authority: CN
Inventors: 陈春勇
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-05-25
Filing date: 2020-05-25
Publication date: 2024-03-12
Anticipated expiration: 2040-05-25
Also published as: CN111654714A

Abstract

The embodiment of the application discloses an information processing method, an information processing device, electronic equipment and a storage medium; according to the method, search content can be obtained, then transaction object information is obtained from a plurality of transaction platforms based on the search content, the transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction objects, correlation factors of the transaction objects are calculated, importance factors of the transaction objects are calculated according to the multidimensional feature information corresponding to the transaction objects, the correlation factors and the importance factors of the transaction objects are fused to obtain ordering parameters of each transaction object for unified ordering, then the transaction objects are ordered according to the ordering parameters of the transaction objects in the transaction object platforms to obtain ordered transaction object sets, and the ordered transaction object sets are output. The scheme can effectively improve the efficiency of selecting the transaction objects.

Description

Information processing method, apparatus, electronic device and storage medium

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to an information processing method, an apparatus, an electronic device, and a storage medium.

Background

With the rapid development of the internet, electronic commerce live broadcast has been developed. The live broadcast of the e-commerce means that the off-line entity home seller promotes and opens the own product client through a live broadcast platform or live broadcast software of the network, so that the client can purchase the own commodity while knowing each performance of the product. The live broadcast of the electronic commerce is a system for helping platform merchants to realize online live broadcast and vending in a live broadcast mode after each electronic commerce opens a live broadcast function.

The advantages of the Internet are absorbed and extended by the live broadcast of the electronic commerce, so that more and more anchor broadcasters begin to conduct live broadcast vending on a live broadcast platform. The skill of the anchor selection is related to the conversion rate of the commodity, but most of the current anchor selection is selected based on manual intuition and historical experience, and the sales data of different platforms does not have comprehensive measurement dimension. The anchor needs a lot of time to select the commodity before living broadcast, and is time-consuming and labor-consuming.

Disclosure of Invention

The embodiment of the application provides an information processing method, an information processing device, electronic equipment and a storage medium, which can effectively improve the efficiency of transaction object selection.

The embodiment of the application provides an information processing method, which comprises the following steps:

Acquiring search content;

transaction object information is obtained from a plurality of transaction platforms based on the search content, wherein the transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction objects;

calculating a relevance factor of the transaction object, and calculating an importance factor of the transaction object according to multi-dimensional feature information corresponding to the transaction object, wherein the relevance factor represents the relevance between the search content and the transaction object, and the importance factor represents the importance of the transaction object;

fusing the relevance factor and the importance factor of the transaction objects to obtain a sorting parameter of each transaction object for unified sorting;

sorting the transaction objects according to sorting parameters of the transaction objects in a plurality of transaction object platforms to obtain a sorted transaction object set;

outputting the ordered transaction object set.

Correspondingly, the embodiment of the application also provides an information processing device, which comprises:

a first acquisition unit configured to acquire search content;

a second obtaining unit, configured to obtain transaction object information from a plurality of transaction platforms based on the search content, where the transaction object information includes a transaction object matched with the search content in each transaction platform, and multidimensional feature information corresponding to the transaction object;

The calculating unit is used for calculating a correlation factor of the transaction object and calculating an importance factor of the transaction object according to multi-dimensional characteristic information corresponding to the transaction object, wherein the correlation factor represents the correlation between the search content and the transaction object, and the importance factor represents the importance of the transaction object;

the fusion unit is used for fusing the correlation factor and the importance factor of the transaction objects to obtain the ordering parameters of each transaction object for unified ordering;

the ordering unit is used for ordering the transaction objects according to ordering parameters of the transaction objects in the transaction object platforms to obtain an ordered transaction object set;

and the output unit is used for outputting the ordered transaction object set.

Optionally, in some embodiments, the computing unit may include a first computing subunit and a second computing subunit, as follows:

the first computing subunit is configured to divide the search content into at least one search keyword, and set a weight of the search keyword; calculating the correlation between the search content and the transaction object according to the search keyword and the weight of the search keyword to obtain a correlation factor of the transaction object;

The second computing subunit is used for acquiring importance preset rules in the transaction platform corresponding to each transaction object; and calculating importance factors of the transaction objects according to the importance preset rules and the multidimensional characteristic information corresponding to each transaction object.

Optionally, in some embodiments, the first calculating subunit may be specifically configured to calculate a relevance between each search keyword and the search content, to obtain a first score of the search keyword; calculating a relevance factor of each search keyword and the transaction object to obtain a second score of the search keyword; and calculating the correlation between the search content and the transaction object according to the weight of the search keyword, the first score of the search keyword and the second score of the search keyword, and obtaining the correlation factor of the transaction object.

Optionally, in some embodiments, the second computing subunit may be specifically configured to determine a weight of each dimension feature object information of the transaction object according to the importance preset rule; and calculating importance factors of the transaction objects according to the weight of each dimension characteristic object information of the transaction objects and the multi-dimension characteristic information.

Optionally, in some embodiments, the fusion unit may include a fusion subunit and a processing subunit, as follows:

the fusion subunit is used for fusing the correlation factor and the importance factor of the transaction object to obtain the comprehensive factor of the transaction object;

and the processing subunit is used for carrying out standardized processing on the comprehensive factors of the transaction objects by using the ordering learning model to obtain ordering parameters of each transaction object for unified ordering.

Optionally, in some embodiments, the processing subunit may be specifically configured to perform feature extraction on the comprehensive factors of the transaction object by using a ranking learning model to obtain comprehensive feature information of the transaction object; and calculating the sorting score of the transaction objects according to the comprehensive characteristic information to obtain the sorting parameters of each transaction object for unified sorting.

Optionally, in some embodiments, the information processing apparatus may further include a training unit, as follows:

the training unit is used for acquiring a plurality of groups of sample data; training a preset ordering learning model by using the sample data to obtain the ordering learning model.

Optionally, in some embodiments, the sorting unit may include a combination subunit and a sorting subunit, as follows:

The combination subunit is used for combining the ordering parameters of the transaction objects in the transaction object platforms to obtain a combined ordering parameter sequence;

and the sequencing subunit is used for sequencing the combined sequencing parameter sequence by using a heap sequencing algorithm to obtain a sequenced transaction object set.

Optionally, in some embodiments, the sorting subunit may be specifically configured to obtain a maximum value of the sorting parameters in the sequence of sorting parameters after combination; exchanging the maximum value of the sorting parameters with the last sorting parameters in the sequence of the sorting parameters after combination to obtain the last sorting parameters after exchange; obtaining the maximum value of the non-exchanged ordering parameters in the ordering parameter sequence after combination; and exchanging the maximum value of the un-exchanged sorting parameters with the last sorting parameter in the un-exchanged sorting parameters in the combined sorting parameter sequence until all sorting parameters in the combined sorting parameter sequence are exchanged, and obtaining a sorted transaction object set.

Optionally, in some embodiments, the second obtaining unit may be specifically configured to obtain interface information of a plurality of transaction platforms; and acquiring transaction object information matched with the search content from the transaction platforms based on the interface information.

Optionally, in some embodiments, the first obtaining unit may be specifically configured to display a search page of the live client, where the search page includes a search input control; determining input search content based on an input operation for the search input control;

the output unit may be specifically configured to display the sorted collection of transaction objects in the search page.

In addition, the embodiment of the application further provides a computer readable storage medium, where a plurality of instructions are stored, where the instructions are adapted to be loaded by a processor to perform steps in any of the information processing methods provided in the embodiments of the application.

In addition, the embodiment of the application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in any information processing method provided by the embodiment of the application when executing the program.

According to the embodiment, search content can be obtained, then transaction object information is obtained from a plurality of transaction platforms respectively based on the search content, wherein the transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction objects, then a correlation factor of the transaction objects is calculated, and an importance factor of the transaction objects is calculated according to the multidimensional feature information corresponding to the transaction objects, wherein the correlation factor represents the correlation between the search content and the transaction objects, the importance factor represents the importance of the transaction objects, then the correlation factor and the importance factor of the transaction objects are fused to obtain a ranking parameter for unified ranking of each transaction object, then the transaction objects are ranked according to the ranking parameter of the transaction objects in the transaction object platforms to obtain a ranked transaction object set, and the ranked transaction object set is output. The scheme can effectively improve the efficiency of selecting the transaction objects.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1a is a schematic view of a scenario of information processing provided by an embodiment of the present application;

FIG. 1b is a first flowchart of an information processing method provided in an embodiment of the present application;

FIG. 2a is a second flowchart of an information processing method provided by an embodiment of the present application;

fig. 2b is a schematic diagram of a desk page in a showcase according to an embodiment of the present application;

FIG. 2c is a schematic diagram of a search page provided in an embodiment of the present application;

FIG. 2d is a schematic illustration of a popularity specification page provided in an embodiment of the present application;

FIG. 2e is a schematic diagram of a merchandise data page provided in an embodiment of the present application;

FIG. 2f is a schematic diagram of a management page provided in an embodiment of the present application;

FIG. 2g is a schematic diagram of an information processing flow provided in an embodiment of the present application;

fig. 3 is a schematic structural view of an information processing apparatus provided in an embodiment of the present application;

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

The principles of the present application are illustrated as implemented in a suitable computing environment. In the following description, specific embodiments of the present application will be described with reference to steps and symbols performed by one or more computers, unless otherwise indicated. Thus, these steps and operations will be referred to in several instances as being performed by a computer, which as referred to herein performs operations that include processing units by the computer that represent electronic signals that represent data in a structured form. This operation transforms the data or maintains it in place in the computer's memory system, which may reconfigure or otherwise alter the computer's operation in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the foregoing text and are not meant to be limiting, and one skilled in the art will recognize that various steps and operations described below may also be implemented in hardware.

The term "unit" as used herein may be considered as a software object executing on the computing system. The various components, units, engines, and services described herein may be viewed as implementing objects on the computing system. The apparatus and method may be implemented in software, but may also be implemented in hardware, which is within the scope of the present application.

The terms "first," "second," and "third," etc. in this application are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to the list of steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The embodiment of the application provides an information processing method, an information processing device, electronic equipment and a storage medium. The information processing apparatus may be integrated in an electronic device, which may be a server or a terminal.

For example, as shown in fig. 1a, first, when a user performs live broadcast selection, the terminal integrated with the information processing apparatus may acquire search content input by the user, then acquire transaction object information from a plurality of transaction platforms based on the search content, where the transaction object information includes a transaction object matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction object, calculate a relevance factor of the transaction object, calculate an importance factor of the transaction object according to the multidimensional feature information corresponding to the transaction object, where the relevance factor characterizes a relevance between the search content and the transaction object, the importance factor characterizes an importance of the transaction object, then fuse the relevance factor and the importance factor of the transaction object to obtain a sorting parameter for unified sorting of each transaction object, then sort the transaction objects according to the sorting parameters of the transaction objects in the transaction object platforms to obtain a sorted transaction object set, and output the sorted transaction object set.

According to the scheme, when the anchor needs to conduct live broadcast selection, search content can be obtained, transaction object information in a plurality of transaction platforms corresponding to the search content is obtained, the transaction objects are ordered after unified processing is conducted on the transaction object information, and an ordered transaction object set is obtained, wherein the ordered transaction object set synthesizes multidimensional characteristic information of the transaction objects, the ordering of the transaction object with the highest conversion rate is provided for the anchor, the limit between the platforms is broken, the anchor is helped to improve the selection efficiency and quality, the cost of the anchor selection is further reduced, and the commodity carrying conversion rate is improved.

The following will describe in detail. The following description of the embodiments is not intended to limit the preferred embodiments.

The present embodiment will be described from the viewpoint of an information processing apparatus which can be integrated in an electronic device which can be a server or a terminal or the like; the terminal may include a mobile phone, a tablet computer, a notebook computer, a personal computer (Personal Computer, PC), and the like.

An information processing method, comprising: obtaining search content, then obtaining transaction object information from a plurality of transaction platforms based on the search content, wherein the transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction objects, then calculating correlation factors of the transaction objects, calculating importance factors of the transaction objects according to the multidimensional feature information corresponding to the transaction objects, wherein the correlation factors represent correlation between the search content and the transaction objects, the importance factors represent the importance of the transaction objects, then fusing the correlation factors and the importance factors of the transaction objects to obtain sorting parameters for unified sorting of each transaction object, then sorting the transaction objects according to the sorting parameters of the transaction objects in the transaction object platforms to obtain a sorted transaction object set, and outputting the sorted transaction object set.

As shown in fig. 1b, the specific flow of the information processing method may be as follows:

101. search content is obtained.

For example, a search page of the live client may be displayed that includes a search input control, with input search content determined based on input operations to the search input control. The expression form of the control can be icons, input boxes, buttons and the like.

For example, a table page in a live show window of a live broadcast client can be displayed, a host can log in an account first before starting live broadcast and commodity carrying of an electronic commerce, one of a plurality of login modes can be selected for logging in, and then a search page of the live broadcast client is displayed to select required commodity carrying. When an input operation of the user for the search input control is detected, input search content is determined.

The middle platform corresponds to the front platform and the back platform, and refers to a shared set of middleware in some systems, which are commonly found in website architecture, financial systems and the like. The middle platform is mainly a platform generated for the front platform, and the only purpose of the middle platform is that the middle platform is better for serving the front platform to carry out large-scale innovation, so that users can be better served, and the enterprise can truly realize continuous butt joint of self-capacity and user requirements.

102. Transaction object information is acquired from the plurality of transaction platforms based on the search content, respectively.

The transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional characteristic information corresponding to the transaction objects. For example, transaction object information matching the search content may be acquired from a plurality of different transaction platforms, such as platform X, platform Y, platform Z, and the like, respectively, according to the search content input by the user.

The transaction platform refers to a platform for providing services for both transaction parties of a transaction object. For example, the transaction platform may be a third party e-commerce platform, which is independent of the provider and the demander of the product or service, and provides services to the buyer and the seller according to specific transaction and service specifications through the network service platform, and the service content may include, but is not limited to, "supply and demand information release and search, establishment of transaction, payment, and logistics". The transaction object refers to an article, such as a commodity, equipment, prop, etc., which are transacted by both transaction parties at a transaction platform.

For example, when the host needs to perform live selection, for example, the transaction object may be a commodity, and the interface information of the multiple transaction platforms may be acquired, so that configuration access is performed according to the interface information, thereby acquiring the transaction object information in the transaction platforms. For example, interface information of a plurality of transaction platforms may be acquired, and transaction object information matching the search content may be acquired from the plurality of transaction platforms based on the interface information. In this case, the transaction platform may be a platform for providing services to both the buyer and the seller of the commodity.

103. And calculating the correlation factor of the transaction object, and calculating the importance factor of the transaction object according to the multidimensional characteristic information corresponding to the transaction object.

Wherein the relevance factor characterizes relevance between the search content and the transaction object, and the importance factor characterizes importance of the transaction object.

There are various ways to calculate the relevance factor of the transaction object, for example, a relevance ranking Model, such as Boolean Model (Vector Space Model), vector space Model (Latent Semantic Analysis), lingo analysis (Latent Semantic Analysis), BM25 (an algorithm for evaluating the relevance between search terms and documents), information retrieval language Model (language Model for information retrieval, LMIR), and so on.

For example, the search content may be specifically divided into at least one search keyword, the weight of the search keyword is set, and the relevance between the search content and the transaction object is calculated according to the search keyword and the weight of the search keyword, so as to obtain the relevance factor of the transaction object.

For example, in some embodiments, the relevance between the search content and the transaction object is calculated according to the search keyword and the weight of the search keyword to obtain the relevance factor of the transaction object, specifically, the relevance between each search keyword and the search content may be calculated to obtain a first score of the search keyword, the relevance factor of each search keyword and the transaction object is calculated to obtain a second score of the search keyword, and the relevance between the search content and the transaction object is calculated according to the weight of the search keyword, the first score of the search keyword and the second score of the search keyword to obtain the relevance factor of the transaction object.

For example, when the transaction object is a commodity, the relevance factor refers to the degree of occurrence of a commodity keyword in a document, and when the degree is higher, the degree of relevance of the document is considered to be higher. The relevance factor is the matching of the title keyword of the commodity and the commodity, the matching degree, whether the important words are matched, the distance between the matched words and the like, and the relevance is possibly affected. For example, when the search content is "duck washing machine", the keyword of a commodity is that the washing machine has higher correlation than the commodity sold by the accessories of the washing machine, and the correlation of the duckling together is higher than the correlation when the duckling is separated from the washing machine.

There are various ways to calculate the importance factor of the transaction object according to the multi-dimensional feature information corresponding to the transaction object, for example, an importance ranking model, such as a web page ranking (PageRank), a hypertext sensitive title search (hyperslink-Induced Topic Search, HITS), a HillTop (a patent for ranking the results of a search engine), a trust index (trust rank), and so on may be used for calculation.

For example, a preset importance rule in a transaction platform corresponding to each transaction object may be obtained, and an importance factor of the transaction object may be calculated according to the preset importance rule and multi-dimensional feature information corresponding to each transaction object.

The setting manner of the importance preset rule may be various, for example, may be set according to the actual application requirement, or may be preset and stored in the electronic device. In addition, the importance preset rule may be built in the electronic device, or may be stored in a memory and transmitted to the electronic device, or the like. The preset importance rules corresponding to each transaction platform may be the same or different, for example, the preset importance rules of the platform X and the preset importance rules of the platform Y may be the same or different, and may be set according to specific situations. Since the multidimensional feature information of each transaction platform may be the same or different, the multidimensional feature information used for calculating the importance factor of each transaction platform may be the same or different, and may be set according to specific situations.

For example, in some embodiments, the importance factor of each transaction object is calculated according to the importance preset rule and the multidimensional feature information corresponding to each transaction object, and specifically, the weight of each dimension feature object information of the transaction object may be determined according to the importance preset rule, and the importance factor of the transaction object is calculated according to the weight of each dimension feature object information of the transaction object and the multidimensional feature information.

For example, when the transaction object is a commodity, the importance factor may include a number of commodity keyword conversions, e.g., the more commodity keyword conversions, the more valuable the commodity. That is, the higher the conversion rate of the keyword of a commodity in all the e-commerce platforms, the higher the importance of the commodity. The importance factor is an element that directly affects commodity conversion rate, such as sales volume and sales price of commodities. The importance factor of the commodity can be one or a combination of a plurality of characteristic factors such as commodity sales, sales price, cost, evaluation, click rate, order conversion rate, exposure rate, amount of orders, and the like.

Where conversion refers to the ratio of all the people arriving at the trading platform and producing purchasing behavior to all the people arriving at the trading platform. The calculation method comprises the following steps: conversion = (number of clients producing purchasing behavior/number of all guests arriving at store) ×100%. Factors affecting conversion may be: baby description, sales goals, evaluation of baby, and so forth.

For example, the comprehensive ordering of the commodities can be related to 10 factors, and the weight of each dimension characteristic object information can be the volume of the transaction: 15%, good score: 10%, collection: 8%, upper and lower frames: 12%, conversion rate: 14%, show window recommendation: 10%, buyback rate: 10%, DSR: the 8% vendor service rating system (Detail Seller Rating) and the like, and the weight ratio of each dimension characteristic object information in the importance factor affecting the ranking can be flexibly set according to actual situations, and is not limited herein.

104. And fusing the relevance factor and the importance factor of the transaction objects to obtain the ordering parameters of each transaction object for unified ordering.

For example, a variety of different feature factors may be combined to give a final keyword to transaction object ranking score. If only one to two of the feature factors are used, some of the most basic ordering of the items may already be done. A better ranking algorithm can be obtained if more feature factors are involved in the ranking. The combined method can be simply and manually configured to a complex Learning model similar to Learning to Rank and the like.

For example, the relevance factor and the importance factor of the transaction object may be fused to obtain a comprehensive factor of the transaction object, and the comprehensive factor of the transaction object is normalized by using the ranking learning model to obtain a ranking parameter of each transaction object for unified ranking.

For example, in some embodiments, the ranking parameters of the transaction objects are obtained according to the ranking learning model, and specifically, feature extraction may be performed on the comprehensive factors of the transaction objects by using the ranking learning model to obtain comprehensive feature information of the transaction objects, and ranking scores of the transaction objects are calculated according to the comprehensive feature information to obtain the ranking parameters of each transaction object for unified ranking.

In order to improve the ranking efficiency, before the ranking learning model is used for carrying out the standardization processing on the comprehensive characteristic information of the transaction objects, the method can further comprise the following steps:

acquiring a plurality of groups of sample data; training a preset ordering learning model by using the sample data to obtain the ordering learning model.

For example, query content may be specifically determined, a plurality of sample transaction object data may be obtained based on the query content, where the sample transaction object data includes a sample transaction object matched with the query content, a feature extraction is performed on the sample transaction object by using a preset ranking learning model to obtain comprehensive feature information of the sample transaction object, a ranking score of the sample transaction object is calculated according to the comprehensive feature information, a sample prediction value is obtained, and the preset ranking learning model is converged according to the sample prediction value and the sample true value, so as to obtain a ranking learning model.

For example, training data may be obtained by way of manual labeling, search logs, and the like. For example, a portion of a Query (Query) may be randomly selected from the search log, giving a trained data evaluator a relevance determination to "Query-URL pair". The common is a grade 5 score, poor, general, good, excellent, perfect. This is used as training data. The manual labeling is subjective judgment of a labeling person and can be influenced by factors such as background knowledge of the labeling person. Where the URL is a uniform resource locator, uniform Resource Locator.

For example, training may be performed using a single document method (Pointwise) in a Rank Learning model (LTR). For example, the Pointwise method only considers the absolute relevance of a single document for a given query, and does not consider the relevance of other documents and the given query. I.e. a sequence of real documents of a given query q, only a single document di and the degree of relevance ci of the query need be considered, i.e. the input data may be in the form of:

105. and sorting the transaction objects according to sorting parameters of the transaction objects in the transaction object platforms to obtain a sorted transaction object set.

For example, the sorting parameters of the transaction objects in the transaction object platforms may be combined to obtain a combined sorting parameter sequence, and the combined sorting parameter sequence is sorted by using a heap sorting algorithm to obtain a sorted transaction object set.

The combination modes can be various, for example, if the same transaction object exists in a plurality of different transaction platforms, the sorting parameters of the different transaction platforms of the transaction object can be processed to obtain processed sorting parameters, if the same transaction object exists in only one transaction platform, the sorting parameters of the transaction object can be directly used as processed sorting parameters, and then the processed sorting parameters of the transaction objects in the plurality of transaction object platforms are combined to obtain a combined sorting parameter sequence. The processing manner may also be various, for example, the parameter weight of each transaction platform may be set, then the sorting parameter and the parameter weight corresponding to the sorting parameter are weighted, so as to obtain the sorting parameter after processing, and so on.

For example, in some embodiments, the combined sorting parameter sequence is sorted by using a heap sort algorithm, and specifically, the maximum value of the sorting parameters in the combined sorting parameter sequence may be obtained; exchanging the maximum value of the sorting parameters with the last sorting parameters in the sequence of the combined sorting parameters to obtain exchanged last sorting parameters; obtaining the maximum value of the non-exchanged ordering parameters in the ordering parameter sequence after combination; and exchanging the maximum value of the un-exchanged sorting parameters with the last sorting parameters in the un-exchanged sorting parameters in the combined sorting parameter sequence until all sorting parameters in the combined sorting parameter sequence are exchanged, and obtaining a sorted transaction object set.

For example, the combined sequence of ordering parameters is structured as a large top heap, where the maximum value of the entire sequence is the root node of the heap top. It is exchanged with the last element, which is at its maximum. The remaining n-1 elements are then reconstructed into a heap, which results in the next smallest value of n elements. By repeating the above steps, an ordered sequence can be obtained.

For example, the initial array a 0 … n-1 may be first built into a maximum stack, which is the initial unordered region, and then the element a 0 with the maximum value is exchanged with the last element a n-1 of the unordered region, so that a new unordered region a 0..n-2 and ordered region a n-1 may be obtained. Since a new root a [0] after swapping may violate heap properties, the current unordered area a [0..n-2] should be adjusted to the largest heap. The element a 0 with the largest median value of a 0 n-2 is then swapped with the last record a n-2 of the interval again, thus yielding new unordered areas a 0 n-3 and ordered areas a n-2 n-1. This is repeated until the unordered area has only one element.

The ordering mode only needs to do n-1 times of ordering, the unordered area always precedes the ordered area in the heap ordering, and the ordered area gradually expands from back to front to the whole vector at the tail of the original vector.

For example, when the transaction object is a commodity, stacking and integrating can be performed according to commodity sorting parameters generated by the sorting learning model, the system recommends the first commodity with the highest commodity popularity value according to a stacking and sorting algorithm, and when the host selects the category of the corresponding commodity, the first commodity with the corresponding category popularity value is also displayed. Thereby generating a ranking of heat values for the commodity of the platform.

The popularity value can be obtained by integrating sales data and heat of the commodity on all electronic commerce platforms for the platform. The popularity value can be composed of data such as commodity sales, sales price, cost, evaluation, click rate, order conversion rate, exposure rate, amount of orders to be placed and the like. Clicking the commodity popularity value Top tag can check the detail of the comprehensive sales data of the commodity on each platform, and the like.

106. Outputting the ordered transaction object set.

For example, a set of ordered transaction objects may be displayed in the search page. The ordered transaction object set displays the popularity value ordering of the corresponding commodity. The anchor can click the corresponding trading object personal value label, display the commodity data page, and view the comprehensive historical sales data details of the commodity on each platform, so as to help the anchor know the sales data of the commodity in more detail.

As can be seen from the foregoing, in this embodiment, search content may be obtained, then, transaction object information may be obtained from a plurality of transaction platforms based on the search content, where the transaction object information includes a transaction object matching the search content in each transaction platform and multidimensional feature information corresponding to the transaction object, then, a correlation factor of the transaction object is calculated, and a importance factor of the transaction object is calculated according to the multidimensional feature information corresponding to the transaction object, where the correlation factor characterizes a correlation between the search content and the transaction object, the importance factor characterizes an importance of the transaction object, then, the correlation factor and the importance factor of the transaction object are fused to obtain a ranking parameter for unified ranking of each transaction object, then, the transaction object is ranked according to the ranking parameters of the transaction objects in the transaction object platforms, a ranked transaction object set is obtained, and the ranked transaction object set is output. According to the scheme, when the anchor needs to conduct live broadcast selection, search content can be obtained, transaction object information in a plurality of transaction platforms corresponding to the search content is obtained, the transaction objects are ordered after unified processing is conducted on the transaction object information, and an ordered transaction object set is obtained, wherein the ordered transaction object set synthesizes multidimensional characteristic information of the transaction objects, the ordering of the transaction object with the highest conversion rate is provided for the anchor, the limit between the platforms is broken, the anchor is helped to improve the selection efficiency and quality, the cost of the anchor selection is further reduced, and the commodity carrying conversion rate is improved.

The method described in the previous embodiment is described in further detail below by way of example.

In this embodiment, the information processing apparatus is specifically integrated in an electronic device, and a transaction object may be a commodity.

Firstly, in order to improve the sorting efficiency, the sorting learning model may be trained first, which may specifically be as follows:

For example, the query content may be specifically determined, a plurality of sample commodity data may be obtained based on the query content, the sample commodity data includes sample commodities matched with the query content, feature extraction is performed on the sample commodities by using a preset ranking learning model to obtain comprehensive feature information of the sample commodities, ranking scores of the sample commodities are calculated according to the comprehensive feature information, a sample predicted value is obtained, and a preset ranking learning model is converged according to the sample predicted value and the sample true value to obtain the ranking learning model.

For example, training data may be obtained by way of manual labeling, search logs, and the like. For example, a portion of Query may be randomly selected from the search log, giving a trained data evaluator a relevance determination for "Query-URL pair". The common is a grade 5 score, poor, general, good, excellent, perfect. This is used as training data. The manual labeling is subjective judgment of a labeling person and can be influenced by factors such as background knowledge of the labeling person.

For example, training may be performed using a single document method (Pointwise) in a Rank Learning model (LTR). For example, the Pointwise method only considers the absolute relevance of a single document for a given query, and does not consider the relevance of other documents and the given query. That is, given a sequence of real documents of query q, only a single document di and the degree of relevance ci of the query need be considered, i.e., the input data is in the form of:

and thirdly, ordering the commodities of a plurality of trading platforms through a trained ordering learning model, and particularly referring to fig. 2a and 2g.

As shown in fig. 2a, a specific flow of an information processing method may be as follows:

201. the electronic device displays a search page of the live client.

Wherein the search page includes a search input control. The representation of the control may be specifically an input box.

For example, the electronic device may specifically display a show window middle page of the live client, as shown in fig. 2b, before the anchor starts the live delivery of the electronic commerce, the anchor may log in the account in an "XXX login" manner, and then display a search page of the live client, as shown in fig. 2c, where the anchor may input the goods to be selected in an input box of the search page, that is, a goods name box.

202. The electronic device determines input search content based on an input operation for the search input control. For example, when detecting an input operation of the user on the search input control, the electronic device determines search content input by the user and acquires the search content, for example, "a man T-shirt" is input by the user, and the acquired search content is "a man T-shirt".

203. The electronic device obtains commodity information from the plurality of transaction platforms based on the search content, respectively.

The commodity information comprises commodities matched with the search content in each transaction platform and multidimensional characteristic information corresponding to the commodities. The multidimensional feature information of the commodity can comprise commodity sales, sales price, cost, evaluation, click rate, order conversion rate, exposure rate, amount of orders, and the like.

For example, a system of the electronic device generates feature factors of the commodity according to the heat of the commodity, and the feature factors generate an algorithm model according to the relevance and the importance. The live show window center table can be accessed to commodities of different e-commerce platforms, such as platform X, platform Y, platform Z and the like. Different e-commerce platforms have a set of commodity ordering algorithms belonging to the e-commerce platforms. When the data is accessed, if only sales data fields on each platform are simply displayed, the comprehensive conditions of the man-machine value and the conversion rate of the commodity are difficult to visually see. Thus, the system generates a characteristic factor for the commodity based on the attribute of the commodity. When the anchor selects the goods, the system calculates each commodity in real time according to the algorithm model, and sorts the commodities according to the score. The algorithm model basically calculates a score for the commodity according to the importance and the relevance of the commodity, and then ranks the commodity. The relevance and importance here are two important factors in the commodity ordering model.

For example, when the host needs to perform live selection, for example, interface information of a plurality of transaction platforms can be obtained, so that configuration access is performed according to the interface information, and commodity information in the transaction platforms is obtained. For example, the electronic device may specifically obtain interface information of a plurality of transaction platforms, and obtain, based on the interface information, merchandise information matching the search content from the plurality of transaction platforms, such as platform X, platform Y, and the like. In this case, the transaction platform may be a platform for providing services to both the buyer and the seller of the commodity.

204. The electronic device calculates a relevance factor for the commodity.

Wherein the relevance factor characterizes relevance between the search content and the commodity. For example, the electronic device may specifically divide the search content into at least one search keyword, set a weight of the search keyword, then calculate a relevance between each search keyword and the search content to obtain a first score of the search keyword, calculate a relevance factor between each search keyword and the commodity to obtain a second score of the search keyword, and calculate a relevance between the search content and the commodity according to the weight of the search keyword, the first score of the search keyword, and the second score of the search keyword to obtain a relevance factor of the commodity.

For example, the relevance factor may refer to the degree to which a keyword of a commodity appears in a document, and when this degree is higher, the higher the relevance of the document is considered. The relevance factor is the matching of the title keyword of the commodity and the commodity, the matching degree, whether the important words are matched, the distance between the matched words and the like, and the relevance is possibly affected. For example, when the search content is "duck washing machine", the keyword of a commodity is that the washing machine has higher correlation than the commodity sold by the accessories of the washing machine, and the correlation of the duckling together is higher than the correlation when the duckling is separated from the washing machine.

205. And the electronic equipment calculates the importance factor of the commodity according to the multi-dimensional characteristic information corresponding to the commodity.

Wherein the importance factor characterizes the importance of the commodity. For example, the electronic device may specifically obtain a preset importance rule in a transaction platform corresponding to each commodity, then determine a weight of each dimension feature object information of the commodity according to the preset importance rule, and calculate an importance factor of the commodity according to the weight of each dimension feature object information of the commodity and the multidimensional feature information.

The setting manner of the importance preset rule may be various, for example, may be set according to the actual application requirement, or may be preset and stored in the electronic device. In addition, the importance preset rule may be built in the electronic device, or may be stored in a memory and transmitted to the electronic device, or the like.

For example, the importance factor may include a number of commodity keyword conversions, e.g., the more commodity keyword conversions, the more valuable the commodity. In particular, the higher the conversion rate of the keyword of a commodity on all the e-commerce platforms, the higher the importance of the commodity. The importance factor is an element that directly affects commodity conversion rate, such as commodity sales, sales price, cost, evaluation, click rate, order conversion rate, exposure rate, amount of orders for goods, and the like, and can be the importance factor of the goods.

For example, the weight of each dimension feature object information in the platform X may be: volume of the success: 15%, good score: 10%, collection: 8%, upper and lower frames: 12%, conversion rate: 14%, show window recommendation: 10%, buyback rate: 10%, DSR:8% vendor service rating system (Detail Seller Rating), etc., the weight of each dimension feature object information in platform Y may be: volume of the success: 20%, good score: 15%, collection: 10%, conversion rate: 12%, show window recommendation: 10%, buyback rate: 10%, DSR:8% vendor service rating system (Detail Seller Rating), and so on.

206. And the electronic equipment fuses the correlation factor and the importance factor of the commodity to obtain the ordering parameters of each commodity for unified ordering.

For example, the electronic device may specifically fuse the relevance factor and the importance factor of the commodity to obtain a comprehensive factor of the commodity, perform feature extraction on the comprehensive factor of the commodity by using a ranking learning model to obtain comprehensive feature information of the commodity, and calculate a ranking score of the commodity according to the comprehensive feature information to obtain a ranking parameter of each commodity for unified ranking.

For example, the background of the electronic device requests the third party platform (i.e. the transaction platform) server to return corresponding commodity data, and the data of the multiple platforms are recombined through a heap ordering algorithm to generate the heat ordering of the commodities. For example, the background requests the third party platform server to return corresponding commodity data, and performs heap sorting and integration according to commodity correlation scores generated by the algorithm model, so as to generate the commodity heat value sorting of the platform.

207. The electronic equipment sorts the commodities according to sorting parameters of the commodities in the commodity platforms to obtain a sorted commodity set.

For example, the electronic device may specifically combine the sorting parameters of the commodities in the multiple commodity platforms to obtain a combined sorting parameter sequence, and sort the combined sorting parameter sequence by using a heap sort algorithm to obtain a sorted commodity set.

For example, if the same commodity has a plurality of different transaction platforms, the sorting parameters of the different transaction platforms of the commodity can be processed to obtain the processed sorting parameters, if the same commodity only has one transaction platform, the sorting parameters of the commodity can be directly used as the processed sorting parameters, and then the processed sorting parameters of the commodity in the commodity platforms are combined to obtain the combined sorting parameter sequence. For example, the processing manner may be to set a parameter weight of each transaction platform, then weight the sorting parameter and a parameter weight corresponding to the sorting parameter to obtain a processed sorting parameter, and so on.

For example, in some embodiments, the electronic device may specifically obtain a maximum value of the sorting parameters in the sequence of sorting parameters after combination, exchange the maximum value of the sorting parameters with an end sorting parameter in the sequence of sorting parameters after combination to obtain an end sorting parameter after exchange, obtain a maximum value of an un-exchanged sorting parameter in the sequence of sorting parameters after combination, exchange the maximum value of the un-exchanged sorting parameter with an end sorting parameter in the un-exchanged sorting parameter in the sequence of sorting parameters after combination until all sorting parameters in the sequence of sorting parameters after combination are exchanged to obtain the ordered commodity set.

For example, the electronic device may perform heap sorting and integration according to the commodity sorting parameters generated by the sorting learning model, and the system recommends Top3 commodity with the highest popularity value of the commodity class according to the heap sorting algorithm, as shown in fig. 2c, when the anchor selects the category of the corresponding class, the Top3 commodity with the popularity value of the corresponding category is also displayed. Thereby generating a ranking of heat values for the commodity of the platform. The user can click on the popularity label to display a popularity specification page, as shown in fig. 2d, the data sources: the popularity value can be obtained by integrating the sales data and the popularity of the commodity on all the electronic commerce platforms for the platforms. Data composition: the popularity value can be composed of data such as commodity sales, sales price, cost, evaluation, click rate, order conversion rate, exposure rate, amount of orders to be placed and the like. The popularity value Top tag: clicking the commodity popularity value Top tag can check the detail of the comprehensive sales data of the commodity on each platform, and the like.

208. The electronic device displays the ordered commodity set in the search page.

For example, the ordered set of items may be displayed in the search page. The ordered commodity set shows the popularity value ordering of the corresponding commodities. The anchor can click on the corresponding commodity popularity value Top tag, and can display a commodity data page, as shown in fig. 2e, and can check the comprehensive historical sales data details of the commodity on each platform, so as to help the anchor know the sales data of the commodity in more detail. When the anchor finishes adding the commodity, the added commodity can be displayed in a management page of a table in the shop window, as shown in fig. 2f, the anchor can need the commodity with the commodity in the management page of the shop window.

For example, the ranking data of the merchandise may be stored to a server, and when the anchor selects the merchandise, the electronic device may request the data from the server and display the popularity ranking of the corresponding merchandise data. The anchor may select the corresponding commodity ordering value according to the need.

As can be seen from the foregoing, in this embodiment, search content may be obtained, then, transaction object information may be obtained from a plurality of transaction platforms based on the search content, where the transaction object information includes a transaction object matching the search content in each transaction platform and multidimensional feature information corresponding to the transaction object, then, a correlation factor of the transaction object is calculated, and a importance factor of the transaction object is calculated according to the multidimensional feature information corresponding to the transaction object, where the correlation factor characterizes a correlation between the search content and the transaction object, the importance factor characterizes an importance of the transaction object, then, the correlation factor and the importance factor of the transaction object are fused to obtain a ranking parameter for unified ranking of each transaction object, then, the transaction object is ranked according to the ranking parameters of the transaction objects in the transaction object platforms, a ranked transaction object set is obtained, and the ranked transaction object set is output. According to the scheme, when a host needs to conduct live broadcast selection, search content can be obtained, transaction object information in a plurality of transaction platforms corresponding to the search content is obtained, the transaction objects are ordered after unified processing is conducted on the transaction object information, and an ordered transaction object set is obtained.

In order to better implement the above method, correspondingly, the embodiment of the application also provides an information processing device, which can be specifically integrated in an electronic device, wherein the electronic device can be a server or a terminal.

For example, as shown in fig. 3, the information processing apparatus may include a first acquisition unit 301, a second acquisition unit 302, a calculation unit 303, a fusion unit 304, a sorting unit 305, and an output unit 306, as follows:

a first acquisition unit 301 for acquiring search content;

a second obtaining unit 302, configured to obtain transaction object information from a plurality of transaction platforms based on the search content, where the transaction object information includes a transaction object matched with the search content in each transaction platform, and multidimensional feature information corresponding to the transaction object;

a calculating unit 303, configured to calculate a relevance factor of the transaction object, and calculate an importance factor of the transaction object according to the multidimensional feature information corresponding to the transaction object, where the relevance factor characterizes a relevance between the search content and the transaction object, and the importance factor characterizes an importance of the transaction object;

The fusion unit 304 is configured to fuse the relevance factor and the importance factor of the transaction objects to obtain a ranking parameter of each transaction object for unified ranking;

a sorting unit 305, configured to sort the transaction objects according to sorting parameters of the transaction objects in the multiple transaction object platforms, so as to obtain a sorted transaction object set;

an output unit 306, configured to output the ordered transaction object set.

Alternatively, in some embodiments, the computing unit 303 may include a first computing subunit and a second computing subunit, as follows:

a first computing subunit, configured to divide the search content into at least one search keyword, and set a weight of the search keyword; calculating the correlation between the search content and the transaction object according to the search keyword and the weight of the search keyword to obtain a correlation factor of the transaction object;

Optionally, in some embodiments, the second computing subunit may be specifically configured to determine a weight of each dimension feature object information of the transaction object according to the preset importance rule; and calculating importance factors of the transaction objects according to the weight of each dimension characteristic object information of the transaction objects and the multi-dimension characteristic information.

Alternatively, in some embodiments, the fusion unit 304 may include a fusion subunit and a processing subunit, as follows:

Optionally, in some embodiments, the information processing apparatus may further include a training unit 307, as follows:

Alternatively, in some embodiments, the ordering unit 305 may include a combination subunit and an ordering subunit, as follows:

and the sorting subunit is used for sorting the combined sorting parameter sequence by using a heap sorting algorithm to obtain a sorted transaction object set.

Optionally, in some embodiments, the sorting subunit may be specifically configured to obtain a maximum value of the sorting parameters in the sequence of sorting parameters after combination; exchanging the maximum value of the sorting parameters with the last sorting parameters in the sequence of the combined sorting parameters to obtain exchanged last sorting parameters; obtaining the maximum value of the non-exchanged ordering parameters in the ordering parameter sequence after combination; and exchanging the maximum value of the un-exchanged sorting parameters with the last sorting parameters in the un-exchanged sorting parameters in the combined sorting parameter sequence until all sorting parameters in the combined sorting parameter sequence are exchanged, and obtaining a sorted transaction object set.

Optionally, in some embodiments, the second obtaining unit 302 may be specifically configured to obtain interface information of multiple transaction platforms; transaction object information matched with the search content is acquired from the transaction platforms based on the interface information.

Optionally, in some embodiments, the first obtaining unit 301 may be specifically configured to display a search page of the live client, where the search page includes a search input control; determining input search content based on an input operation for the search input control;

the output unit 306 may be specifically configured to display the ordered collection of transaction objects in the search page.

In the implementation, each unit may be implemented as an independent entity, or may be implemented as the same entity or several entities in any combination, and the implementation of each unit may be referred to the foregoing method embodiment, which is not described herein again.

As can be seen from the foregoing, in this embodiment, the first acquiring unit 301 may acquire the search content, the second acquiring unit 302 may acquire the transaction object information from the plurality of transaction platforms based on the search content, where the transaction object information includes the transaction object matched with the search content in each transaction platform and the multidimensional feature information corresponding to the transaction object, the calculating unit 303 may calculate the correlation factor of the transaction object, and calculate the importance factor of the transaction object according to the multidimensional feature information corresponding to the transaction object, where the correlation factor characterizes the correlation between the search content and the transaction object, the importance factor characterizes the importance of the transaction object, the fusion unit 304 may fuse the correlation factor and the importance factor of the transaction object to obtain a sorting parameter for unified sorting of each transaction object, and the sorting unit 305 may sort the transaction objects according to the sorting parameters of the transaction objects in the plurality of transaction object platforms to obtain the sorted transaction object set, and the output unit 306 may be used to output the sorted transaction object set. According to the scheme, when the anchor needs to conduct live broadcast selection, search content can be obtained, transaction object information in a plurality of transaction platforms corresponding to the search content is obtained, the transaction objects are ordered after unified processing is conducted on the transaction object information, and an ordered transaction object set is obtained, wherein the ordered transaction object set synthesizes multidimensional characteristic information of the transaction objects, the ordering of the transaction object with the highest conversion rate is provided for the anchor, the limit between the platforms is broken, the anchor is helped to improve the selection efficiency and quality, the cost of the anchor selection is further reduced, and the commodity carrying conversion rate is improved.

In addition, the embodiment of the application further provides an electronic device, as shown in fig. 4, which shows a schematic structural diagram of the electronic device according to the embodiment of the application, specifically:

the electronic device may include one or more processing cores 'processors 401, one or more computer-readable storage media's memory 402, power supply 403, and input unit 404, among other components. Those skilled in the art will appreciate that the electronic device structure shown in fig. 4 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components. Wherein:

the processor 401 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 402, and calling data stored in the memory 402, thereby controlling the electronic device as a whole. Optionally, processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, an application program, etc., and the modem processor mainly processes wireless communication. It will be appreciated that the modem processor described above may not be integrated into the processor 401.

The memory 402 may be used to store software programs and modules, and the processor 401 executes various functional applications and data processing by executing the software programs and modules stored in the memory 402. The memory 402 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the electronic device, etc. In addition, memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

The electronic device further comprises a power supply 403 for supplying power to the various components, preferably the power supply 403 may be logically connected to the processor 401 by a power management system, so that functions of managing charging, discharging, and power consumption are performed by the power management system. The power supply 403 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.

The electronic device may further comprise an input unit 404, which input unit 404 may be used for receiving input digital or character information and generating keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.

Although not shown, the electronic device may further include a display unit or the like, which is not described herein. In particular, in this embodiment, the processor 401 in the electronic device loads executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 executes the application programs stored in the memory 402, so as to implement various functions as follows:

obtaining search content, then obtaining transaction object information from a plurality of transaction platforms based on the search content, wherein the transaction object information comprises transaction objects matched with the search content in each transaction platform and multi-dimensional characteristic information corresponding to the transaction objects, calculating correlation factors of the transaction objects, calculating importance factors of the transaction objects according to the multi-dimensional characteristic information corresponding to the transaction objects, wherein the correlation factors represent correlation between the search content and the transaction objects, the importance factors represent the importance of the transaction objects, then fusing the correlation factors and the importance factors of the transaction objects to obtain a sorting parameter for unified sorting of each transaction object, sorting the transaction objects according to the sorting parameters of the transaction objects in the transaction object platforms to obtain a sorted transaction object set, and outputting the sorted transaction object set.

The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.

As can be seen from the foregoing, in this embodiment, search content may be obtained, then, transaction object information may be obtained from a plurality of transaction platforms based on the search content, where the transaction object information includes a transaction object matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction object, then, a correlation factor of the transaction object is calculated, and a importance factor of the transaction object is calculated according to the multidimensional feature information corresponding to the transaction object, where the correlation factor characterizes a correlation between the search content and the transaction object, the importance factor characterizes an importance of the transaction object, then, the correlation factor and the importance factor of the transaction object are fused to obtain a ranking parameter for unified ranking of each transaction object, then, the transaction object is ranked according to the ranking parameter of the transaction object in the transaction object platforms, a ranked transaction object set is obtained, and a ranked transaction object set is output. According to the scheme, when the anchor needs to conduct live broadcast selection, search content can be obtained, transaction object information in a plurality of transaction platforms corresponding to the search content is obtained, the transaction objects are ordered after unified processing is conducted on the transaction object information, and an ordered transaction object set is obtained, wherein the ordered transaction object set synthesizes multidimensional characteristic information of the transaction objects, the ordering of the transaction object with the highest conversion rate is provided for the anchor, the limit between the platforms is broken, the anchor is helped to improve the selection efficiency and quality, the cost of the anchor selection is further reduced, and the commodity carrying conversion rate is improved.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, embodiments of the present application also provide a storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform steps in any of the information processing methods provided by the embodiments of the present application. For example, the instructions may perform the steps of:

Wherein the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.

The instructions stored in the storage medium may perform steps in any information processing method provided in the embodiments of the present application, so that the beneficial effects that any information processing method provided in the embodiments of the present application can be achieved, which are detailed in the previous embodiments and are not described herein.

The foregoing has described in detail the methods, apparatuses, electronic devices and storage media for processing information provided by the embodiments of the present application, and specific examples have been applied to illustrate the principles and embodiments of the present application, where the foregoing examples are only used to help understand the methods and core ideas of the present application; meanwhile, those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, and the present description should not be construed as limiting the present application in view of the above.

Claims

1. An information processing method, characterized by comprising:

acquiring search content;

acquiring interface information of a plurality of transaction platforms based on the search content, and performing configuration access according to the interface information to obtain transaction object information in the transaction platforms, wherein the transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction objects;

calculating a relevance factor of the transaction object, acquiring a importance preset rule in a transaction platform corresponding to each transaction object, and calculating the importance factor of the transaction object according to the importance preset rule and multi-dimensional characteristic information corresponding to each transaction object, wherein the relevance factor represents the relevance between the search content and the transaction object, and the importance factor represents the importance of the transaction object;

Outputting the ordered transaction object set.

2. The method of claim 1, wherein said calculating a relevance factor for the transaction object comprises:

dividing the search content into at least one search keyword, and setting the weight of the search keyword;

and calculating the correlation between the search content and the transaction object according to the search keyword and the weight of the search keyword to obtain a correlation factor of the transaction object.

3. The method according to claim 2, wherein the calculating the relevance between the search content and the transaction object according to the search keyword and the weight of the search keyword, to obtain the relevance factor of the transaction object, includes:

calculating the relativity of each search keyword and the search content to obtain a first score of the search keyword;

calculating a relevance factor of each search keyword and the transaction object to obtain a second score of the search keyword;

and calculating the correlation between the search content and the transaction object according to the weight of the search keyword, the first score of the search keyword and the second score of the search keyword, and obtaining the correlation factor of the transaction object.

4. The method according to claim 1, wherein calculating the importance factor of each transaction object according to the importance preset rule and the corresponding multidimensional feature information of the transaction object comprises:

determining the weight of each dimension characteristic object information of the transaction object according to the importance preset rule;

and calculating importance factors of the transaction objects according to the weight of each dimension characteristic object information of the transaction objects and the multi-dimension characteristic information.

5. The method of claim 1, wherein the fusing the relevance factor and the importance factor of the transaction objects to obtain a ranking parameter for uniform ranking for each transaction object comprises:

fusing the correlation factor and the importance factor of the transaction object to obtain the comprehensive factor of the transaction object;

and carrying out standardized processing on the comprehensive factors of the transaction objects by using a sequencing learning model to obtain sequencing parameters of each transaction object for unified sequencing.

6. The method of claim 5, wherein normalizing the composite factors of the transaction objects using a ranking learning model to obtain ranking parameters for uniform ranking for each transaction object comprises:

Extracting features of the comprehensive factors of the transaction objects by using a sequencing learning model to obtain comprehensive feature information of the transaction objects;

and calculating the sorting score of the transaction objects according to the comprehensive characteristic information to obtain the sorting parameters of each transaction object for unified sorting.

7. The method of claim 5, further comprising, prior to said normalizing said transaction object's integrated characteristic information using a rank learning model:

acquiring a plurality of groups of sample data;

training a preset ordering learning model by using the sample data to obtain the ordering learning model.

8. The method of claim 1, wherein the sorting the transaction objects according to the sorting parameters of the transaction objects in the plurality of transaction object platforms to obtain the sorted collection of transaction objects comprises:

combining the sorting parameters of the transaction objects in the transaction object platforms to obtain a combined sorting parameter sequence;

and sequencing the combined sequencing parameter sequence by using a heap sequencing algorithm to obtain a sequenced transaction object set.

9. The method of claim 8, wherein ranking the combined ranking parameter sequence using a heap ranking algorithm results in a ranked set of transaction objects, comprising:

Obtaining the maximum value of the sorting parameters in the combined sorting parameter sequence;

exchanging the maximum value of the sorting parameters with the last sorting parameters in the sequence of the sorting parameters after combination to obtain the last sorting parameters after exchange;

obtaining the maximum value of the non-exchanged ordering parameters in the ordering parameter sequence after combination;

and exchanging the maximum value of the un-exchanged sorting parameters with the last sorting parameter in the un-exchanged sorting parameters in the combined sorting parameter sequence until all sorting parameters in the combined sorting parameter sequence are exchanged, and obtaining a sorted transaction object set.

10. The method of claim 1, wherein the acquiring transaction object information from the plurality of transaction platforms based on the search content, respectively, comprises:

acquiring interface information of a plurality of transaction platforms;

and acquiring transaction object information matched with the search content from the transaction platforms based on the interface information.

11. The method of claim 1, wherein the obtaining search content comprises:

displaying a search page of a live client, wherein the search page comprises a search input control;

Determining input search content based on an input operation for the search input control;

the output ordered collection of transaction objects includes: displaying the ordered transaction object set in the search page.

12. An information processing apparatus, characterized by comprising:

a first acquisition unit configured to acquire search content;

the second acquisition unit is used for respectively acquiring interface information of a plurality of transaction platforms based on the search content, and performing configuration access according to the interface information to obtain transaction object information in the transaction platforms, wherein the transaction object information comprises transaction objects matched with the search content in each transaction platform and multidimensional feature information corresponding to the transaction objects;

the computing unit is used for computing the correlation factor of the transaction objects, acquiring an importance preset rule in a transaction platform corresponding to each transaction object, and computing the importance factor of the transaction objects according to the importance preset rule and multi-dimensional characteristic information corresponding to each transaction object, wherein the correlation factor represents the correlation between the search content and the transaction objects, and the importance factor represents the importance of the transaction objects;

and the output unit is used for outputting the ordered transaction object set.

13. A computer readable storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor to perform the steps in the information processing method of any one of claims 1 to 11.

14. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 11 when the program is executed.