WO2019037258A1

WO2019037258A1 - Information recommendation method, device and system, and computer-readable storage medium

Info

Publication number: WO2019037258A1
Application number: PCT/CN2017/108761
Authority: WO
Inventors: 王健宗; 吴天博; 黄章成; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-08-20
Filing date: 2017-10-31
Publication date: 2019-02-28
Also published as: CN107526800B; CN107526800A

Abstract

An information recommendation method, device and system and a computer-readable storage medium. The information recommendation device comprises a memory and a processor. The memory is stored with an information recommendation system therein. When being executed by the processor, the information recommendation system is configured to: determine, before recommending a target object to a user, an approximate object corresponding to the target object according to a mapping relationship between a preset target object and the approximate object, and use both the target object and the determined approximate object to be objects for analysis (S1); acquire, from a preset data source, evaluation data performed by each user on the objects for analysis in a predetermined time, and perform analysis on the evaluation data of each object for analysis according to a preset analysis rule to obtain a positive tag value corresponding to each object for analysis (S2); if the positive tag value is greater than or equal to a preset threshold, determine whether to recommend the target object to the user according to a preset recommendation algorithm (S3); and if so, determine that the user is a related user to the target object, and recommend the target object to the user (S4). The above information recommendation system can recommend customized information to a user accurately.

Description

Information recommendation device, method, system and computer readable storage medium

Priority claim

This application is based on the priority of the Paris Convention, which is entitled to the Chinese Patent Application No. CN 201710715452.1, entitled "Information Recommended Device, Method and Computer Readable Storage Media", filed on August 20, 2017, the entire Chinese patent application The content is incorporated herein by reference.

Technical field

The present application relates to the field of communications technologies, and in particular, to an apparatus, method, system, and computer readable storage medium for information recommendation.

Background technique

In the current personalized information recommendation scheme, the user's interest points are mainly obtained by analyzing the user's historical behavior, and then various information is pushed according to the user's interest points. The historical behavior of the user can be obtained through social media (such as Weibo, blog, forum, podcast, etc.) used by the user. In the prior art, the user's positive preference is obtained by analyzing the historical behavior of the user, for example, user A is on Weibo. On the release of more travel and food texts and photos, you can analyze that user A's positive preference is to like travel and food. However, on the one hand, the user's historical behavior is limited, and it is not accurate to recommend personalized information to the user based on the historical behavior of the user's Internet access; on the other hand, the user's interest is relatively stable, and the social background is in dynamic change (for example, current Hot news or time has occurred, and it is not possible to accurately recommend personalized information to users simply by analyzing the user's historical behavior.

Summary of the invention

The purpose of the present application is to provide an apparatus, method, system and computer readable storage medium for information recommendation, which are intended to accurately recommend personalized information to a user.

To achieve the above object, the present application provides an apparatus for information recommendation, the information recommendation apparatus comprising a memory and a processor connected to the memory, wherein the memory stores information recommended to be run on the processor. The system, when the information recommendation system is executed by the processor, implements the following steps:

S1. Before recommending the target object to the user, determining an approximate object corresponding to the target object according to a mapping relationship between the predetermined target object and the approximated object, and using the target object and the determined approximated object as the object to be analyzed;

S2. Acquire, from a predetermined data source, evaluation data of each object to be analyzed by the user in a preset time, analyze the evaluation data of each object to be analyzed according to a predetermined analysis rule, and obtain a positive corresponding to each object to be analyzed. To the tag value;

S3, if the forward label value is greater than or equal to a preset threshold, determining, according to a predetermined recommendation algorithm, whether to recommend the target object to the user;

S4, if yes, determining that the user is a related user of the target object, and pushing the user Recommend the target object.

To achieve the above objective, the present application further provides a method for information recommendation, and the method for information recommendation includes:

S4. If yes, determining that the user is a related user of the target object, and recommending the target object to the user.

To achieve the above objective, the present application further provides a system for information recommendation, where the information recommendation system includes:

a first determining module, configured to determine an approximate object corresponding to the target object according to a mapping relationship between the predetermined target object and the approximated object, and use the target object and the determined approximated object as the target object before recommending the target object to the user Object to be analyzed;

An analysis module, configured to obtain, from a predetermined data source, evaluation data of the object to be analyzed by each user in a preset time, analyze the evaluation data of each object to be analyzed according to a predetermined analysis rule, and obtain each object to be analyzed Corresponding forward tag value;

a second determining module, configured to determine, according to a predetermined recommendation algorithm, whether to recommend the target object to the user, if the forward label value is greater than or equal to a preset threshold;

And a recommendation module, if yes, determining that the user is a related user of the target object, and recommending the target object to the user.

The application further provides a computer readable storage medium on which a system for information recommendation is stored, and when the system of information recommendation is executed by the processor, the steps are implemented:

The beneficial effects of the present application are: before recommending a target object to a user, the present application determines an approximate object corresponding to the target object according to a predetermined mapping relationship, and treats both the target object and the approximated object as The analysis object is obtained, and the evaluation data of each object to be analyzed by the user within a preset time is obtained from the predetermined data source, and the evaluation data of each object to be analyzed is analyzed according to a predetermined analysis rule, and the forward label corresponding to each object to be analyzed is obtained. a value; when the forward label value is greater than or equal to the preset threshold, determining whether to recommend the target object to the user according to a predetermined recommendation algorithm to recommend the target object to the user, the present application is based on analyzing the evaluation data of the target object and the approximate object. The way to recommend the target object to the user can accurately recommend personalized information to the user.

DRAWINGS

1 is a schematic diagram of an optional application environment of each embodiment of the present application;

2 is a schematic diagram of a hardware architecture of an apparatus for information recommendation in FIG. 1;

3 is a schematic structural diagram of a predetermined structure word segmentation tree;

FIG. 4 is a schematic flowchart of an embodiment of a method for information recommendation according to the present application.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

It should be noted that the descriptions of "first", "second" and the like in the present application are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. . Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly. In addition, the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.

FIG. 1 is a schematic diagram of an optional application environment of each embodiment of the present application. In this embodiment, the present application is applicable to an application environment including, but not limited to, device 1, user terminal 2, and network 3 of information recommendation. The device 1 for information recommendation communicates with one or more user terminals 2 via the network 3.

The user terminal 2 includes, but is not limited to, any electronic product that can interact with a user through a keyboard, a mouse, a remote controller, a touch panel, or a voice control device, for example, a personal computer, a tablet, or a smart phone. , Personal Digital Assistant (PDA), game consoles, Internet Protocol Television (IPTV), smart wearable devices, navigation devices, etc., or mobile devices such as digital TVs, desktop computers, Fixed terminal for notebooks, servers, etc.

The network 3 may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), or a 4G. Network, 5G network Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.

The device 1 for information recommendation is a device capable of automatically performing numerical calculation and/or information processing in accordance with an instruction set or stored in advance. The information recommendation device 1 may be a computer, a single network server, a server group composed of multiple network servers, or a cloud-based cloud composed of a large number of hosts or network servers, where cloud computing is a distributed computing one. Kind, a super virtual computer consisting of a group of loosely coupled computers.

Referring to FIG. 2, which is a schematic diagram of an optional hardware architecture of the device 1 for information recommendation in FIG. 1. In this embodiment, the device 1 for information recommendation may include, but is not limited to, a memory 11 that can be communicably connected to each other through a system bus. The processor 12 and the network interface 13. It should be noted that Figure 2 only shows the device 1 with information recommendations for components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead. .

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), and a random access memory (RAM). , static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like. In some embodiments, the memory 11 may be an internal storage unit of the device 1 recommended by the information, such as a hard disk or memory of the device 1 recommended by the information. In other embodiments, the memory 11 may also be an external storage device of the information recommendation device 1, such as a plug-in hard disk equipped with the device 1 recommended by the information, and a smart memory card (Smart Media Card, SMC). ), Secure Digital (SD) card, Flash Card, etc. Of course, the memory 11 can also include both the internal storage unit of the device 1 for information recommendation and its external storage device. In this embodiment, the memory 11 is generally used to store a system installed in the device 1 for information recommendation and various types of application software, such as program codes of the system for information recommendation. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.

The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 12 is typically used to control the overall operation of the device 1 for information recommendation, such as performing control and processing associated with communication with the user terminal 2. In this embodiment, the processor 12 is configured to run program code or process data stored in the memory 11, such as a system that runs the information recommendation.

The network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the device 1 for information recommendation and other electronic devices. In this embodiment, the network interface 13 is mainly used to connect the device 1 recommended by the information to one or more of the user terminals 2 through the network 3 to establish a data transmission channel and a communication connection.

The information recommendation system is stored in the memory 11 and includes at least one computer readable instruction stored in the memory 11, the at least one computer readable instruction being executable by the processor 12 to implement information of embodiments of the present application. a preferred method; and the at least one computer readable instruction can be classified into different logic modules according to functions implemented by the various portions thereof, including the first The fixed module, the analysis module, the second determining module and the recommended module.

Wherein, the system recommended by the above information is implemented by the processor 12 to implement the following steps:

Step S1: Before recommending the target object to the user, determining an approximate object corresponding to the target object according to a mapping relationship between the predetermined target object and the approximated object, and using the target object and the determined approximated object as the object to be analyzed;

The target object may be location information (such as restaurant location, library location or attraction location, etc.), item information (such as clothing accessories, books or daily necessities, etc.), network information (such as news, websites or audio and video, etc.), and financial management. Information (such as stocks, securities, insurance products, etc.). The approximate object is an object similar to the target object. For example, if the target object is a restaurant location, the corresponding approximate object may be a food street location, a restaurant location, etc., and the target object is a library location, and the corresponding approximate object may be a variety of bookstores. Location, bookstore location, museum location, etc.

In this embodiment, the target object and its corresponding approximation object are mapped in advance, and the mapping relationship between the target object and the approximation object is stored, for example, in the form of a mapping table. Before the target object is recommended to the user, the approximate object that has a mapping relationship with the target object is obtained according to the target object, and both the target object and the approximated object are used as subsequent objects to be analyzed.

In step S2, the evaluation data of each object to be analyzed by each user in a preset time is obtained from a predetermined data source, and the evaluation data of each object to be analyzed is analyzed according to a predetermined analysis rule, and corresponding to each object to be analyzed is obtained. Forward tag value;

In this embodiment, the predetermined data source is, for example, a catering platform, a microblog website, a forum website, a life insurance server, etc., and the evaluation data includes a comment and a rating, and each user is obtained from the predetermined data source to the waiting time within a preset time. The evaluation data made by the analysis object is obtained, for example, from the predetermined data source, the evaluation data of the object to be analyzed by each user within six months is obtained. The forward label value is a value in which the evaluation data is a variable, and the larger the evaluation amount in which the evaluation is high in the evaluation data, the larger the forward label value.

Wherein, the predetermined analysis rule preferably includes:

If the evaluation data of the object to be analyzed is a score, and the score of the object to be analyzed is less than the preset score, determining that the object to be analyzed is an invalid object, or if the score is greater than or equal to the preset score, determining that the evaluation data is a positive information class And calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule;

If the evaluation data of the object to be analyzed is a comment, parsing the core viewpoint information corresponding to the comment, and using the pre-trained classifier to identify the information pointing category corresponding to the core viewpoint information (where the information pointing category includes the positive information category, Negative information class), if the information pointing category is a negative information class, determining that the object to be analyzed is an invalid object, or if the information pointing category is a positive information class, calculating the object to be analyzed according to a preset calculation rule The forward tag value corresponding to the evaluation data.

Step S3, if the forward label value is greater than or equal to a preset threshold, determining whether to recommend the target object to the user according to a predetermined recommendation algorithm;

Step S4, if yes, determining that the user is a related user of the target object, and recommending the target object to the user.

In this embodiment, a predetermined threshold is preset, when the forward label value of the object to be analyzed is greater than or equal to When the threshold is preset, it indicates that the object to be analyzed is better or is recognized by the user, and further considers whether to recommend the target object to the user according to a predetermined recommendation algorithm, and the forward label value of the object to be analyzed is smaller than the preset. When the threshold is exceeded, the target object is not considered to be recommended to the user.

In this embodiment, the predetermined recommendation algorithm may be: calculating a recommended value based on the forward label value, and determining, by the recommended value, whether to recommend the target object to the user, specifically, when the recommended value is greater than or equal to the preset recommended value, The target object is recommended to the user, for example, the recommendation information of the target object is sent to the user, and the target object is not recommended to the user when the recommended value is less than the preset recommended value.

Compared with the prior art, the present embodiment determines an approximate object corresponding to the target object according to a predetermined mapping relationship before recommending the target object to the user, and takes both the target object and the approximated object as objects to be analyzed; from a predetermined data source. Obtaining evaluation data of each user to be analyzed in a preset time, analyzing the evaluation data of each object to be analyzed according to a predetermined analysis rule, and obtaining a forward label value corresponding to each object to be analyzed; when the forward label value is greater than or equal to Determining a threshold value, determining whether to recommend the target object to the user according to a predetermined recommendation algorithm, to recommend the target object to the user, and the present embodiment recommends the target object to the user based on the method of analyzing the evaluation data of the target object and the approximate object, Accurately recommend personalized information to users.

In a preferred embodiment, on the basis of the foregoing embodiment, when the information recommendation system is executed by the processor 12 to implement the step S3, the method specifically includes:

If the forward label value is greater than or equal to the preset threshold, the recommended value of the target object is calculated based on the forward label value and the recommended value calculation formula constructed by the user data of the object to be analyzed, where the recommended value is greater than When the value is equal to the preset recommendation value, the target object is recommended to the user, where the recommended value is calculated as:

P(o|u,t)=λP(o|u)+(1−λ)P(o|δ _t ),

Wherein P(o|u, t) is a recommended value of the user u to the target object or the approximate object o in the background t, the λ is a weight (λ is a constant), and the P(o|u) is The forward label value of the user u to the target object or the approximate object o, the P(o|δ _t ) is the probability that the target object or the approximate object o is selected by the user u, and δ _t represents the topic distribution under the time series background t, C={c1, c2, . . . , cn} is the user-generated content of the user u in the context of the time t, n is the number of user-generated content, and the sim(.) is a user-generated content (eg, the user-published micro The rating of the blog or forum, etc.) is similar to the target object, and the ow={w1, w2, ..., wm} is the keyword set of the target object o. Take the Weibo platform as an example, you can simply understand:

Each topic must be "active". If this period is t, then the set of microblogs published by user u during this period is C={c1,c2,...,cn}, and the target object is set. The keyword set is ow={w1,w2,...,wm}, then you get:

Where sim(.) represents the similarity between a microblog and a target object, preferably, this embodiment adopts The value of the similarity is calculated using a TF-IDF (term frequency–inverse document frequency) model.

In a preferred embodiment, based on the foregoing embodiment, the foregoing preset calculation rule includes: calculating a first quantity of the evaluation data of the positive information category that the user makes for the object to be analyzed, and the a second quantity of evaluation data belonging to the positive information category made by the user for all objects to be analyzed; a third quantity of evaluation data belonging to the positive information category made by all users for the object to be analyzed, and all users for all The fourth quantity of the evaluation data belonging to the positive information class made by the object to be analyzed; dividing the first quantity by the second quantity to obtain the first forward label parameter, and substituting the third quantity and the fourth quantity into the preset formula

To derive a second forward label parameter,

For the second forward label parameter, the B is the third quantity, and the A is the fourth quantity; the first forward label parameter is multiplied by the second forward label parameter, and the evaluation data corresponding to the object to be analyzed is obtained. Forward tag value.

Preferably, the classifier is a support vector machine classifier, and the training process of the classifier includes:

Get a core sample of positive information (eg, 10,000) positive information categories (eg, a wide range of Ping An health insurance coverage, fast safe car brands), and a core number of negative (eg 4000) negative information categories Information samples (for example, Ping An Auto Insurance claims slow service, Ping An wealth management products have no promised high), randomize all core opinion information samples into a first preset ratio (for example, 70%) of the training set and the second preset ratio ( For example, 30%) of the verification set, use the training set to train the classifier, and use the verification set to verify the accuracy of the trained classifier. If the accuracy is greater than or equal to the preset accuracy (eg, 0.98), the training ends, or, If the accuracy rate is less than the preset accuracy rate, the number of core view information samples of the positive information class and the core view information samples of the negative information class are increased, and the training is resumed until the accuracy of the trained classifier is greater than or equal to the preset accuracy rate. The training is over.

In a preferred embodiment, on the basis of the foregoing embodiment, when the information recommendation system is executed by the processor 12 to implement the step of parsing the core view information corresponding to the comment, the method specifically includes:

Performing word segmentation on the comment, and performing part-of-speech tagging on each participle corresponding to the comment;

According to the order and part of speech of the respective word segment corresponding to the comment, each word segment corresponding to the comment is constructed into a preset structure word segmentation tree, and each piece segment corresponding to the comment is constructed into a preset structure word segment tree to parse out the core point information corresponding to the comment. .

Among them, the participles include words and words, for example, for the comment "** launched *** products are very good", after the word segmentation results are "**", "launch", "of", "***", "very ", "good."

Preferably, the word segmentation processing of the comment comprises: splitting the comment by a preset type punctuation mark (for example: ",", ".", "!", ";", etc.), for example, from the comment The information between the starting position and the first preset type punctuation is a short sentence; if there is no preset type punctuation at the end of the comment, from the last preset type punctuation to the end of the comment The message is a short sentence and is punctuated from the first preset type punctuation to the last last preset type punctuation The information between the symbols, the information between each of the two preset types of punctuation marks is a short sentence; if the end position of the comment has a preset type punctuation, the first punctuation from the first preset type to the first from the last The information between the preset type punctuation marks, and the information between each of the two preset types of punctuation marks is a short sentence.

For each short sentence of the split, the long word priority principle is used to continue the word segmentation: for example, the long word priority principle means: for a phrase T1 that requires a word segment, starting with the first word A, from the pre-stored thesaurus Find the longest word X1 starting from A, then remove X1 from T1 and leave T2, and then use the same segmentation principle for T2. The result after segmentation is "X1/X2/, ,,,,, For example, the result of the review "Ping An has introduced the product of Zunhong Life" is "Peace" / "Publish" / "Yes" / "Zhonghong Life" / "Product".

Preferably, performing part-of-speech tagging on each participle corresponding to the comment includes:

According to the mapping relationship between words and words in the universal word dictionary library and part of speech (for example, in the universal word dictionary library, the part of speech corresponding to the playground is a noun), and/or the mapping relationship between the preset words and words and the part of speech (for example, In the mapping relationship between the preset words and words and the part of speech, the part of speech corresponding to the playground is a common noun), and the part of speech corresponding to each participle of each short sentence is determined, wherein the part of the word and the word are mapped to the part of speech. The labeling priority is higher than the mapping between words and words in the universal word dictionary library and part of speech. Among them, part of speech includes: real words (nouns, verbs, adjectives, quantifiers, pronouns, etc.), virtual words (adverbs, prepositions, conjunctions, auxiliary words, interjections, onomatopoeia, etc.).

Label the corresponding part of speech for each participle, for example, for the participle "了", "来来", "着", "过过", "的", "地地", "得得", "似像", "所", etc. Mark the part of the word as a supporting word; for the words "very safe", "guaranteed type", "high income", "long period", etc., the wording is an adjective.

Preferably, according to the order and part of speech of each participle corresponding to the comment, constructing each participle corresponding to the comment into a preset structure word segmentation tree includes:

As shown in FIG. 3, the preset structure word segmentation tree includes a multi-level node, the first-level node is the comment itself, and the second-level node is a participle phrase obtained by the comment according to the order of the corresponding participle and part of speech (for example, Noun phrase, verb phrase, etc.), each level node after the second level node is obtained by the segmentation phrase of the upper level node according to the part of speech, until it is divided into the last level node of each node branch. In the process of division, if a participle phrase cannot be further divided, the participle phrase is the last level node of the node branch where it is located, and “I go to the playground to play football”, and the preset structure word segmentation tree is constructed as shown in Fig. 3. Show.

The node distance between each first keyword segmentation word (for example, a noun) and each second keyword segmentation word (for example, an adjective) is calculated based on the preset structure word segmentation tree, and the node distance is the first keyword segmentation word segmentation and the second The number of nodes separated by the keyword participles respectively acquires a second keyword segmentation word having the smallest distance from each first keyword segmentation node, and the second keyword segmentation words with the smallest distance between each first keyword segmentation word and the node are in accordance with the comment. The order in the composition constitutes the corresponding core viewpoint information.

As shown in FIG. 4, FIG. 4 is a schematic flowchart of a method for recommending information in the application, and the method for recommending information includes the following steps:

Wherein, the predetermined analysis rule preferably includes:

In this embodiment, a preset threshold is preset. When the value of the forward label of the object to be analyzed is greater than or equal to the preset threshold, it indicates that the object to be analyzed is better evaluated or recognized by the user, and further considered according to a predetermined recommendation algorithm. Recommend the target object to the user, when the forward target of the object to be analyzed When the sign value is less than the preset threshold, the target object is not considered to be recommended to the user.

The present application also provides a computer readable storage medium having stored thereon a system for information recommendation, the step of implementing the above-described information recommendation method when the information recommendation system is executed by the processor.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

An information recommendation device, wherein the information recommendation device comprises a memory and a processor connected to the memory, wherein the memory stores a system for recommending information that can be run on the processor, where When the system for information recommendation is executed by the processor, the following steps are implemented:

S1. Before recommending the target object to the user, determining an approximate object corresponding to the target object according to a mapping relationship between the predetermined target object and the approximated object, and using the target object and the determined approximated object as the object to be analyzed;

S2. Acquire, from a predetermined data source, evaluation data of each object to be analyzed by the user in a preset time, analyze the evaluation data of each object to be analyzed according to a predetermined analysis rule, and obtain a positive corresponding to each object to be analyzed. To the tag value;

S3, if the forward label value is greater than or equal to a preset threshold, determining, according to a predetermined recommendation algorithm, whether to recommend the target object to the user;

S4. If yes, determining that the user is a related user of the target object, and recommending the target object to the user.
The apparatus for information recommendation according to claim 1, wherein the evaluation data comprises a rating and a comment, and the predetermined analysis rule comprises:

If the evaluation data of the object to be analyzed is a score, and the score of the object to be analyzed is less than the preset score, determining that the object to be analyzed is an invalid object, or if the score is greater than or equal to the preset score, determining that the evaluation data is a positive information class And calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule;

If the evaluation data of the object to be analyzed is a comment, parsing the core viewpoint information corresponding to the comment, and using the pre-trained classifier to identify the information pointing category corresponding to the core viewpoint information, if the information pointing category is a negative information category And determining that the object to be analyzed is an invalid object, or if the information is directed to a category of positive information, calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule.
The device for information recommendation according to claim 2, wherein when the system for information recommendation is executed by the processor to implement the step S3, the method specifically includes:

If the forward label value is greater than or equal to the preset threshold, the recommended value of the target object is calculated based on the forward label value and the recommended value calculation formula constructed by the user data of the object to be analyzed, where the recommended value is greater than When the value is equal to the preset recommendation value, the target object is recommended to the user, where the recommended value is calculated as:

P(o|u,t)=λP(o|u)+(1−λ)P(o|δ t ),
The P(o|u, t) is a recommended value of the user u to the target object or the approximate object o in the background t, the λ is a weight, and the P(o|u) is the user u to the target object. Or approximating the forward label value of the object o, the P(o|δ t ) is the probability that the target object or the approximate object o is selected by the user u, and the δ t represents the topic distribution under the time series background t, the C= {c1, c2, ..., cn} generates content for the user of the user u in the context of the time t, the n is the number of content generated by the user, and the sim(.) is the similarity between the content generated by the user and the target object. The ow={w1, w2, . . . , wm} is a keyword set of the target object o.
The device for information recommendation according to claim 2, wherein the preset calculation rule comprises:

Calculating a first quantity of the evaluation data belonging to the positive information category made by the user for the object to be analyzed, and a second quantity of the evaluation data belonging to the positive information category made by the user for all objects to be analyzed;

Calculating a third quantity of evaluation data belonging to the positive information category made by all users for the object to be analyzed, and a fourth quantity of evaluation data belonging to the positive information category made by all users for all objects to be analyzed;

Obtaining a first forward label parameter based on the first quantity and the second quantity, acquiring a second forward label parameter based on the third quantity, the fourth quantity, and a preset formula, and based on the first forward label parameter And the second forward label parameter is used to obtain a forward label value corresponding to the evaluation data of the object to be analyzed, wherein the preset formula is:
Said
For the second forward tag parameter, the B is a third quantity and the A is a fourth quantity.
The device for information recommendation according to claim 2, wherein when the system for information recommendation is executed by the processor to implement the step of parsing the core viewpoint information corresponding to the comment, the method specifically includes:

Performing word segmentation on the comment, and performing part-of-speech tagging on each participle corresponding to the comment;

According to the order and part of speech of the respective word segment corresponding to the comment, each word segment corresponding to the comment is constructed into a preset structure word segmentation tree, and each piece segment corresponding to the comment is constructed into a preset structure word segment tree to parse out the core point information corresponding to the comment. .
The apparatus for information recommendation according to claim 5, wherein the step of the information recommendation system being executed by the processor to perform the word segmentation processing on the comment comprises:

According to the preset type punctuation marks, the comment is split into short sentences, and for each short sentence of the split, the long word priority principle is used to continue the word segmentation;

The step of the information recommendation system being performed by the processor to implement the part-of-speech tagging of each participle corresponding to the comment includes:

According to the mapping relationship between words and words in the universal word dictionary library and the part of speech, and/or the mapping relationship between the preset words and words and the part of speech, the part of speech corresponding to each participle is determined and marked, wherein the preset words and The part-of-speech tagging relationship between the word-to-speech and the part-of-speech is higher than the word-to-speech and word-of-speech in the universal word dictionary.
The apparatus for information recommendation according to claim 6, wherein the preset structure word segmentation tree comprises a multi-level node, the first-level node is the comment, and the second-level node is the corresponding participle by the comment. The word segmentation phrase obtained by the order and part of speech segmentation, each level node after the second-level node is obtained by word segmentation of the segmentation phrase of the upper-level node.
The device for information recommendation according to claim 7, wherein said information recommendation The system is executed by the processor to implement the step of constructing, according to the respective word segment corresponding to the comment, a core structure information corresponding to the comment to be configured as a preset structure word segmentation tree, including:

Calculating a node distance of each first keyword segmentation word and each second keyword segmentation word based on the constructed preset structure word segmentation tree; respectively acquiring a second keyword segmentation word with the smallest distance from each first keyword keyword segmentation node, respectively, and each first key The second keyword segmentation with the least-word segmentation and the node distance constitutes the corresponding core viewpoint information in the order in the comment.
A method for information recommendation, characterized in that the method for recommending information includes:

S1. Before recommending the target object to the user, determining an approximate object corresponding to the target object according to a mapping relationship between the predetermined target object and the approximated object, and using the target object and the determined approximated object as the object to be analyzed;

S2. Acquire, from a predetermined data source, evaluation data of each object to be analyzed by the user in a preset time, analyze the evaluation data of each object to be analyzed according to a predetermined analysis rule, and obtain a positive corresponding to each object to be analyzed. To the tag value;

S3, if the forward label value is greater than or equal to a preset threshold, determining, according to a predetermined recommendation algorithm, whether to recommend the target object to the user;

S4. If yes, determining that the user is a related user of the target object, and recommending the target object to the user.
The method of information recommendation according to claim 9, wherein the evaluation data comprises a rating and a comment, and the predetermined analysis rule comprises:

If the evaluation data of the object to be analyzed is a score, and the score of the object to be analyzed is less than the preset score, determining that the object to be analyzed is an invalid object, or if the score is greater than or equal to the preset score, determining that the evaluation data is a positive information class And calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule;

If the evaluation data of the object to be analyzed is a comment, parsing the core viewpoint information corresponding to the comment, and using the pre-trained classifier to identify the information pointing category corresponding to the core viewpoint information, if the information pointing category is a negative information category And determining that the object to be analyzed is an invalid object, or if the information is directed to a category of positive information, calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule.
The method of information recommendation according to claim 10, wherein the step S3 comprises:

If the forward label value is greater than or equal to the preset threshold, the recommended value of the target object is calculated based on the forward label value and the recommended value calculation formula constructed by the user data of the object to be analyzed, where the recommended value is greater than When the value is equal to the preset recommendation value, the target object is recommended to the user, where the recommended value is calculated as:

P(o|u,t)=λP(o|u)+(1−λ)P(o|δ t ),
The P(o|u, t) is a recommended value of the user u to the target object or the approximate object o in the background t, the λ is a weight, and the P(o|u) is the user u to the target object. Or approximating the forward label value of the object o, the P(o|δ t ) is the probability that the target object or the approximate object o is selected by the user u, and the δ t represents the topic distribution under the time series background t, the C= {c1, c2, ..., cn} generates content for the user of the user u in the context of the time t, the n is the number of content generated by the user, and the sim(.) is the similarity between the content generated by the user and the target object. The ow={w1, w2, . . . , wm} is a keyword set of the target object o.
The method of information recommendation according to claim 10, wherein the preset calculation rule comprises:

Calculating a first quantity of the evaluation data belonging to the positive information category made by the user for the object to be analyzed, and a second quantity of the evaluation data belonging to the positive information category made by the user for all objects to be analyzed;

Calculating a third quantity of evaluation data belonging to the positive information category made by all users for the object to be analyzed, and a fourth quantity of evaluation data belonging to the positive information category made by all users for all objects to be analyzed;

Obtaining a first forward label parameter based on the first quantity and the second quantity, acquiring a second forward label parameter based on the third quantity, the fourth quantity, and a preset formula, and based on the first forward label parameter And the second forward label parameter is used to obtain a forward label value corresponding to the evaluation data of the object to be analyzed, wherein the preset formula is:
Said
For the second forward tag parameter, the B is a third quantity and the A is a fourth quantity.
The method of information recommendation according to claim 10, wherein the step of parsing out the core viewpoint information corresponding to the comment comprises:

Performing word segmentation on the comment, and performing part-of-speech tagging on each participle corresponding to the comment;

According to the order and part of speech of the respective word segment corresponding to the comment, each word segment corresponding to the comment is constructed into a preset structure word segmentation tree, and each piece segment corresponding to the comment is constructed into a preset structure word segment tree to parse out the core point information corresponding to the comment. .
The method of information recommendation according to claim 13, wherein the step of performing word segmentation processing on the comment comprises:

According to the preset type punctuation marks, the comment is split into short sentences, and for each short sentence of the split, the long word priority principle is used to continue the word segmentation;

The step of the information recommendation system being performed by the processor to implement the part-of-speech tagging of each participle corresponding to the comment includes:

According to the mapping relationship between words and words in the universal word dictionary library and the part of speech, and/or the mapping relationship between the preset words and words and the part of speech, the part of speech corresponding to each participle is determined and marked, wherein the preset words and The part-of-speech tagging relationship between the word-to-speech and the part-of-speech is higher than the word-to-speech and word-of-speech in the universal word dictionary.
The method of information recommendation according to claim 14, wherein the predetermined structure word segmentation tree comprises a multi-level node, the first-level node is the comment, and the second-level node is the corresponding part-of-score by the comment The word segmentation phrase obtained by the order and part of speech segmentation, each level node after the second-level node is obtained by word segmentation of the segmentation phrase of the upper-level node.
The method of information recommendation according to claim 15, wherein the step of constructing, based on the respective participles corresponding to the comment, the core view information corresponding to the comment by the preset structure word segmentation tree comprises:

Calculating a node distance of each first keyword segmentation word and each second keyword segmentation word based on the constructed preset structure word segmentation tree; respectively acquiring a second keyword segmentation word with the smallest distance from each first keyword keyword segmentation node, respectively, and each first key The second keyword segmentation with the least-word segmentation and the node distance constitutes the corresponding core viewpoint information in the order in the comment.
A system for information recommendation, characterized in that the system for recommending information includes:

a first determining module, configured to determine an approximate object corresponding to the target object according to a mapping relationship between the predetermined target object and the approximated object, and use the target object and the determined approximated object as the target object before recommending the target object to the user Object to be analyzed;

An analysis module, configured to obtain, from a predetermined data source, evaluation data of the object to be analyzed by each user in a preset time, analyze the evaluation data of each object to be analyzed according to a predetermined analysis rule, and obtain each object to be analyzed Corresponding forward tag value;

a second determining module, configured to determine, according to a predetermined recommendation algorithm, whether to recommend the target object to the user, if the forward label value is greater than or equal to a preset threshold;

And a recommendation module, if yes, determining that the user is a related user of the target object, and recommending the target object to the user.
The system of information recommendation according to claim 17, wherein said evaluation data comprises a rating and a comment, said predetermined analysis rule comprising:

If the evaluation data of the object to be analyzed is a score, and the score of the object to be analyzed is less than the preset score, determining that the object to be analyzed is an invalid object, or if the score is greater than or equal to the preset score, determining that the evaluation data is a positive information class And calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule;

If the evaluation data of the object to be analyzed is a comment, parsing the core viewpoint information corresponding to the comment, and using the pre-trained classifier to identify the information pointing category corresponding to the core viewpoint information, if the information pointing category is a negative information category And determining that the object to be analyzed is an invalid object, or if the information is directed to a category of positive information, calculating a forward label value corresponding to the evaluation data of the object to be analyzed according to a preset calculation rule.
The information recommendation system according to claim 18, wherein the second determining module is specifically configured to:

If the forward label value is greater than or equal to the preset threshold, the recommended value of the target object is calculated based on the forward label value and the recommended value calculation formula constructed by the user data of the object to be analyzed, where the recommended value is greater than When the value is equal to the preset recommendation value, the target object is recommended to the user, where the recommended value is calculated as:

P(o|u,t)=λP(o|u)+(1−λ)P(o|δ t ),
The P(o|u, t) is a recommended value of the user u to the target object or the approximate object o in the background t, the λ is a weight, and the P(o|u) is the user u to the target object. Or approximating the forward label value of the object o, the P(o|δ t ) is the probability that the target object or the approximate object o is selected by the user u, and the δ t represents the topic distribution under the time series background t, the C= {c1, c2, ..., cn} generates content for the user of the user u in the context of the time t, the n is the number of content generated by the user, and the sim(.) is the similarity between the content generated by the user and the target object. The ow={w1, w2, . . . , wm} is a keyword set of the target object o.
A computer readable storage medium, wherein the computer readable storage medium stores a system for information recommendation, the information recommendation system being executed by a processor to implement the method of any one of claims 9-16 The steps of the information recommended method.