CN112507229A - Document recommendation method and system and computer equipment - Google Patents
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Abstract
The application relates to a document recommendation method, a document recommendation system and computer equipment. The document recommendation method comprises the following steps: a document information acquisition step, which defines and records user behavior and acquires document information operated by a user within a certain time period; a similar document acquisition step, namely indexing according to the document information to acquire similar documents; and a document recommendation step, namely performing hot sequencing on the similar documents according to a hot document algorithm and recommending the hot documents to the user. By combining the user behavior and the document popularity to recommend the document, the problem of poor recommendation effect is solved, and personalized document recommendation is realized.
Description
Technical Field
The present application relates to the field of document recommendation technologies, and in particular, to a document recommendation method, system, and computer device.
Background
With the development of internet technology, the amount of information on the internet has increased explosively. In order to make it easier and faster for users to obtain such information, recommendation techniques are widely used in information systems. The basic idea of the associated recommendation technology is to find out correlations between different information based on one or more characteristics of the information, further establish a relationship between the information, and recommend information having a relationship with the information to a user when the user browses the information.
The research focus on the associated recommendation technology is to mine more features available for recommendation and how to establish the relationship between information according to the features in practical application. At present, a more common way is to establish a relationship between information according to user behaviors, taking document recommendation as an example, the interests of users can be analyzed according to historical behavior records of browsing, searching and the like of the users on documents, then a relationship between the documents is established according to the interest similarity degree of a single user or a plurality of users, and finally document recommendation is performed according to the established relationship. The above method has a problem of poor recommendation.
Under the background, a document recommendation method is provided, which comprehensively considers user behaviors and document popularity to solve the problems in the prior art.
Disclosure of Invention
The embodiment of the application provides a file recommendation method, a file recommendation system and computer equipment, wherein the file recommendation method comprises the following steps of defining specific behaviors of a user: download, collection, browse and like behavior, and document attributes: tags, authors, and categories, document recommendations are made both in terms of user behavior and document popularity. To at least address the problem of personalized recommendations in the related art.
In a first aspect, an embodiment of the present application provides a document recommendation method, including the following steps:
a document information acquisition step, which defines and records user behavior and acquires document information operated by the user within a certain time period;
a similar document obtaining step, namely indexing according to the document information to obtain a similar document;
and a document recommendation step, namely performing hot sorting on the similar documents according to a hot document algorithm and recommending the hot documents to the user.
In some of these embodiments, the document recommendation step comprises:
a user behavior weight obtaining step, namely setting the weights of different user behaviors according to the expression intention of the user behavior and in combination with the data recall condition;
a document popularity calculation step, calculating the popularity of the similar documents according to the weight and an exponential decay function;
and a hot document acquisition step, namely sequencing the similar documents according to the heat degree and returning the hot documents.
In some embodiments, the document popularity calculation step comprises:
the exponential decay function is set as
decay＝0.99t
Wherein t represents the release time of the similar document, the value range of the decade is [0.5,1], and when the decade exceeds the range, the decade is equal to 0.5 or 1;
the expression of the heat is as follows:
score＝total*decay
wherein the total represents the hotness of the user behavior.
In some embodiments, the degree of hotness of the user behavior is set as the sum of the products of the user behavior and the corresponding weight.
In some embodiments, the similar document acquiring step specifically includes:
an information acquisition step, wherein the document information is acquired from a document library, and the document information comprises but is not limited to an author, a label and a category;
and a document indexing step, namely acquiring the similar documents in the document library, which are the same as any one or combination of the author, the tag and the category of the document information, according to index query.
In a second aspect, an embodiment of the present application provides a document recommendation system, where the document recommendation method according to the first aspect is applied, and includes:
the document information acquisition module defines and records user behaviors, and acquires document information operated by the user within a certain time period;
the similar document acquisition module is used for indexing according to the document information to acquire similar documents; and the document recommending module is used for hot sequencing the similar documents according to a hot document algorithm and recommending the hot documents to the user.
In some of these embodiments, the document recommendation module comprises:
the user behavior weight acquisition unit is used for setting the weights of different user behaviors according to the expression intention of the user behavior and in combination with the data recall condition;
the document popularity calculation unit calculates the popularity of the similar documents according to the weight and an exponential decay function;
and the popular document acquisition unit is used for sequencing the similar documents according to the popularity and returning the popular documents.
In some embodiments, the document popularity obtaining unit is configured to:
the exponential decay function is set as
decay＝0.99t
Wherein t represents the release time of the similar document, the value range of the decade is [0.5,1], and when the decade exceeds the range, the decade is equal to 0.5 or 1;
the expression of the heat is as follows:
score＝total*decay
and the total represents the heat degree of the user behavior, and the heat degree of the user behavior is set as the sum of the products of the user behavior and the corresponding weight.
In some embodiments, the similar document obtaining module specifically includes:
the information acquisition unit is used for acquiring the document information from a document library, wherein the document information comprises but is not limited to an author, a label and a category;
and the document indexing unit is used for acquiring the similar documents in the document library, which are the same as any one or combination of the author, the tag and the category of the document information, according to index query.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the document recommendation method according to the first aspect is implemented.
Compared with the related art, the document recommendation method provided by the embodiment of the application carries out document recommendation by combining the user behavior and the document popularity, solves the problem of poor recommendation effect, and realizes personalized document recommendation.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow diagram of a document recommendation method according to an embodiment of the present application;
FIG. 2 is a flowchart of document recommendation steps according to an embodiment of the present application;
FIG. 3 is a flowchart of document recommendation steps according to an embodiment of the present application;
FIG. 4 is a flow chart of a document recommendation method according to a preferred embodiment of the present application;
FIG. 5 is a block diagram of a document recommendation system according to an embodiment of the present application;
fig. 6 is a hardware structure diagram of a computer device according to an embodiment of the present application.
Description of the drawings:
1. a document information acquisition module; 2. A similar document acquisition module; 3. A document recommendation module;
31. a user behavior weight obtaining unit; 32. A document heat calculation unit;
33. a hot document acquisition unit; 21. An information acquisition unit;
22. a document indexing unit; 81. A processor; 82. A memory;
83. a communication interface; 80. A bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase 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 ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
Information overload is one of the most serious problems in today's big data environment, and recommendation systems are receiving increasing attention as a method to effectively alleviate this problem. How to fully utilize the user data to further improve the performance and the user satisfaction of the recommendation system becomes a main task of the recommendation system in a big data environment.
In the recommendation system, the user model implements analysis of user interest preferences, which is one of the key technologies for implementing the recommendation system, and is usually constructed by analyzing item contents or user interaction behaviors focused by yoghurt, but the user interest is modeled from different angles by an item content-based or user interaction behavior-based method. The method based on the item content utilizes the item content data browsed by the user to describe the user interest points from the content perspective, and utilizes the user behavior data to mine the relationship between the user behavior and the user interest points based on the method of the user interaction behavior.
The embodiment provides a document recommendation method. Fig. 1 is a flowchart of a document recommendation method according to an embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
a document information acquisition step S1 of defining and recording user behavior, and acquiring document information operated by a user in a certain time period;
a similar document acquisition step S2, wherein the similar document is acquired by indexing according to the document information;
a document recommendation step S3, according to a popular document algorithm, hot-sort the similar documents and recommend the popular documents to the user.
Through the steps, according to the double consideration of the similarity and the popularity of the document, the document recommendation can be carried out according to the requirements of the user, and meanwhile, the popularity condition of the document can be considered.
In practical application, a data structure of user behaviors and document information is designed firstly, wherein the data storage structure of the user behaviors comprises document marks, behaviors, users and time, so as to store user behavior records. The data structure of the document information includes a document unique mark, a document publishing time, a document tag, a document category, a document author, a document praise number, a document download number, a document collection number, and a document browsing number.
The popular documents refer to documents with the popularity ranking n top, wherein n can be set according to actual scenes.
FIG. 2 is a flowchart of a document recommending step according to an embodiment of the present application, as shown in FIG. 2, in some embodiments, the document recommending step S3 includes:
a user behavior weight obtaining step S31, wherein weights of different user behaviors are set according to expression intentions of the user behaviors and in combination with data recall conditions;
a document popularity calculation step S32, calculating the popularity of similar documents according to the weight and an exponential decay function;
a topical document acquisition step S33, sorting similar documents according to degree of hotness, and returning to topical documents. In practical applications, the user behavior includes, but is not limited to, approval, browsing, downloading, and collection. It should be noted that, knowing the latest published document from the user behavior, the document heat is higher, and the document heat is attenuated as the document publishing time increases, but as the document publishing time increases, the influence of the publishing time on the document heat tends to be stable, so the exponential decay function is selected in this embodiment.
In some of these embodiments, the document popularity calculation step S32:
an exponential decay function set to
decay＝0.99t
Wherein t represents the release time of the similar document, the value range of the decade is [0.5,1], and when the decade exceeds the range, the decade is equal to 0.5 or 1;
the expression for heat is:
score＝total*decay
wherein, the total represents the heat degree of the user behavior.
In some of these embodiments, the heat of the user behavior is set to the sum of the products of the user behavior and the corresponding weights.
Fig. 3 is a flowchart of a document recommending step according to an embodiment of the present application, and as shown in fig. 3, in some embodiments, the similar document acquiring step S2 specifically includes:
an information acquisition step S21, acquiring document information from a document library, the document information including but not limited to author, label and category;
and a document indexing step S22, wherein, similar documents in the document library, which are the same as any one or the combination of the author, the label and the category of the document information, are obtained according to the index query.
The embodiments of the present application are described and illustrated below by means of preferred embodiments.
FIG. 4 is a flowchart of a document recommendation method according to a preferred embodiment of the present application, as shown in FIG. 4, the flowchart includes the steps of:
s301, designing a data structure
The document data structure is designed into a document unique mark, a document publishing time, a document tag, a document category, a document author, a document praise number, a document download number, a document collection number and a document browsing number. And storing the document through an Elasticissearch, and establishing an index as a documentIndex.
And designing a user behavior data structure as a document mark, a behavior, a user and time so as to store the user behavior record.
S302, obtaining and storing the document
The latest (defining time as two days) behavior of the user is obtained through the user behavior record table, for example: downloading, collecting and other behaviors to obtain related behaviors; recording as latestbahoviors;
according to the document marks recorded in the latestbahoviors, the related documents and the document details are obtained, for example: document authors, document tags, document categories, etc., noted as allDocuments.
S303, designing the weight of the user behavior
According to the user behavior, different weights are added by designing different behaviors, such as: the praise, the download and the collection are active operation behaviors, the design weight is the same as 2, the browsing behavior weight is reset as 1, and the behavior heat of the user is calculated as follows:
total is document praise 2+ document collection 2+ document download 2+ document browsing
S304, acquiring similar documents
According to the document details (including but not limited to document authors, document tags and document categories) in the allDocuments, querying similar documents with the same authors, the same tags and the same categories or with the same combination from the documentIndex;
s305, obtaining a recommended document
Designing a decay function based on document publishing time as an exponent, setting a base number as 0.99 and setting a power as publishing time, and setting a maximum value and a minimum value as follows: [0.5, 1.0 ]; the release time factor is thus calculated as:
publish_score＝math.pow(0.99,day)；
publish_factor＝max(publish_score,0.5)；
publish_factor＝min(publish_score,1.0)；
calculating the document heat score (total) publish factor;
and performing heat calculation on the hit similar documents according to the document heat algorithm, sorting according to the calculation score, respectively taking the hot documents, recording as hot documents, and randomly taking 5 documents from the hot documents to return to recommendation.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here. For example, the steps S303 and S304 may be interchanged.
The embodiment also provides a document recommendation system, which is used for implementing the above embodiments and preferred embodiments, and the description of the system is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Fig. 5 is a block diagram of a document recommendation system according to an embodiment of the present application, and as shown in fig. 5, the system includes:
the document information acquisition module 1 defines and records user behaviors, and acquires document information operated by a user within a certain time period from the user behaviors;
the similar document acquisition module 2 is used for indexing according to the document information to acquire similar documents;
and the document recommending module 3 is used for hot sequencing similar documents according to a hot document algorithm and recommending the hot documents to the user.
In some of these embodiments, the document recommendation module 3 includes:
the user behavior weight acquiring unit 31 is used for setting the weights of different user behaviors according to the expression intention of the user behavior and in combination with the data recall condition;
the document hot degree calculating unit 32 calculates the hot degree of the similar document according to the weight and an exponential decay function; the topical document acquisition unit 33 sorts the similar documents according to the degree of hotness, and returns the topical documents. In some embodiments, in the document heat acquisition unit 32:
an exponential decay function set to
decay＝0.99t
Wherein t represents the release time of the similar document, the value range of the decade is [0.5,1], and when the decade exceeds the range, the decade is equal to 0.5 or 1;
the expression for heat is:
score＝total*decay
and the total represents the heat degree of the user behavior, and the heat degree of the user behavior is set as the sum of products of the user behavior and the corresponding weight.
In some embodiments, the similar document obtaining module 2 specifically includes:
an information acquisition unit 21 that acquires document information from a document library, the document information including but not limited to author, tag, and category;
the document indexing unit 22 obtains similar documents in the document library, which are the same as any one or combination of authors, tags and categories of the document information, according to the index query.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the document recommendation method described in conjunction with fig. 1 in the embodiment of the present application may be implemented by a computer device. Fig. 6 is a hardware structure diagram of a computer device according to an embodiment of the present application.
The computer device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 realizes any one of the document recommendation methods in the above embodiments by reading and executing computer program instructions stored in the memory 82.
In some of these embodiments, the computer device may also include a communication interface 83 and a bus 80. As shown in fig. 6, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The computer device may execute the heat calculation in the embodiment of the present application based on the obtained similar document, thereby implementing the document recommendation method described in conjunction with fig. 1.
In addition, in combination with the document recommendation method in the foregoing embodiments, the embodiments of the present application may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the document recommendation methods in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A document recommendation method, comprising the steps of:
a document information acquisition step, which defines and records user behavior and acquires document information operated by the user within a certain time period;
a similar document obtaining step, namely indexing according to the document information to obtain a similar document;
and a document recommendation step, namely performing hot sorting on the similar documents according to a hot document algorithm and recommending the hot documents to the user.
2. The document recommendation method according to claim 1, wherein the document recommendation step comprises:
a user behavior weight obtaining step, namely setting the weights of different user behaviors according to the expression intention of the user behavior and in combination with the data recall condition;
a document popularity calculation step, calculating the popularity of the similar documents according to the weight and an exponential decay function;
and a hot document acquisition step, namely sequencing the similar documents according to the heat degree and returning the hot documents.
3. The document recommendation method according to claim 2, wherein in said document popularity calculation step:
the exponential decay function is set as
decay＝0.99t
Wherein t represents the release time of the similar document, the value range of the decade is [0.5,1], and when the decade exceeds the range, the decade is equal to 0.5 or 1;
the expression of the heat is as follows:
score＝total*decay
wherein the total represents the hotness of the user behavior.
4. The document recommendation method according to claim 3, wherein the degree of heat of the user behavior is set as a sum of products of the user behavior and the corresponding weights.
5. The document recommendation method according to claim 1, wherein the similar document acquisition step specifically comprises:
an information acquisition step, wherein the document information is acquired from a document library, and the document information comprises but is not limited to an author, a label and a category;
and a document indexing step, namely acquiring the similar documents in the document library, which are the same as any one or combination of the author, the tag and the category of the document information, according to index query.
6. A document recommendation system applying the document recommendation method according to any one of claims 1 to 5, comprising:
the document information acquisition module defines and records user behaviors, and acquires document information operated by the user within a certain time period;
the similar document acquisition module is used for indexing according to the document information to acquire similar documents;
and the document recommending module is used for hot sequencing the similar documents according to a hot document algorithm and recommending the hot documents to the user.
7. The document recommendation system according to claim 6, wherein the document recommendation module comprises:
the user behavior weight acquisition unit is used for setting the weights of different user behaviors according to the expression intention of the user behavior and in combination with the data recall condition;
the document popularity calculation unit calculates the popularity of the similar documents according to the weight and an exponential decay function;
and the popular document acquisition unit is used for sequencing the similar documents according to the popularity and returning the popular documents.
8. The document recommendation system according to claim 7, wherein the document popularity acquisition unit:
the exponential decay function is set as
decay＝0.99t
Wherein t represents the release time of the similar document, the value range of the decade is [0.5,1], and when the decade exceeds the range, the decade is equal to 0.5 or 1;
the expression of the heat is as follows:
score＝total*decay
and the total represents the heat degree of the user behavior, and the heat degree of the user behavior is set as the sum of the products of the user behavior and the corresponding weight.
9. The document recommendation system according to claim 6, wherein the similar document acquisition module specifically comprises:
the information acquisition unit is used for acquiring the document information from a document library, wherein the document information comprises but is not limited to an author, a label and a category;
and the document indexing unit is used for acquiring the similar documents in the document library, which are the same as any one or combination of the author, the tag and the category of the document information, according to index query.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the document recommendation method of any one of claims 1-5 when executing the computer program.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114936316A (en) * | 2022-04-18 | 2022-08-23 | 上海二三四五网络科技有限公司 | Hacker hot content calculation method based on display position factor improvement |
| CN115630170A (en) * | 2022-12-08 | 2023-01-20 | 中孚安全技术有限公司 | Document recommendation method, system, terminal and storage medium |
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| CN115630170A (en) * | 2022-12-08 | 2023-01-20 | 中孚安全技术有限公司 | Document recommendation method, system, terminal and storage medium |
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