CN110413637B - Information recommendation method, device and equipment - Google Patents
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- CN110413637B CN110413637B CN201910711637.4A CN201910711637A CN110413637B CN 110413637 B CN110413637 B CN 110413637B CN 201910711637 A CN201910711637 A CN 201910711637A CN 110413637 B CN110413637 B CN 110413637B
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- 238000004590 computer program Methods 0.000 claims description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/243—Natural language query formulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses an information recommendation method, device and equipment. Wherein the method comprises the following steps: and determining target information of the target user through the acquired characteristic data of the target user and a first model obtained through training of the characteristic data extracted from the database, and recommending the target information to the target user. Therefore, various characteristics of the user and the information can be integrated by means of the first model to make decisions, and the information can be recommended more accurately while correlation between the user and the information is measured from a single dimension in the prior art is overcome.
Description
Technical Field
The embodiment of the invention relates to an information processing technology, in particular to an information recommending method, an information recommending device and information recommending equipment.
Background
In the conventional information recommendation process, the information to be recommended is usually evaluated by using a collaborative filtering algorithm, but this method only measures the similarity between the user and the information through the direct action of the user, for example, most of users playing album A play album B, so that album A is similar to album B, and the similarity between album A and album B is not evaluated from the information content, thereby affecting the accuracy of information recommendation and failing to produce better user experience.
Disclosure of Invention
The invention provides an information recommending method, device and equipment, which can integrate various characteristics of users and information to make decisions, and can accurately recommend information while overcoming the defect that the correlation between the users and the information is measured from a single dimension in the prior art.
In a first aspect, an embodiment of the present invention provides an information recommendation method, where the method includes:
acquiring characteristic data of a target user;
determining target information of a target user according to the feature data and the first model;
wherein the first model is obtained by training based on the feature data extracted from the database;
and recommending the target information to the target user.
In a second aspect, an embodiment of the present invention further provides an information recommendation apparatus, where the apparatus includes:
the acquisition module is used for acquiring the characteristic data of the target user;
the determining module is used for determining target information of a target user according to the characteristic data and the first model;
wherein the first model is obtained by training based on the feature data extracted from the database;
and the recommending module is used for recommending the target information to the target user.
In a third aspect, an embodiment of the present invention further provides an information recommendation apparatus, including:
the information recommendation method provided in the first embodiment of the invention is implemented when the processor executes the computer program.
In a fourth aspect, the embodiment of the present invention further provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements an information recommendation method as provided by the embodiment of the present invention.
The embodiment of the invention provides an information recommending method, device and equipment, which are used for determining target information of a target user through acquired characteristic data of the target user and a first model obtained by training the characteristic data extracted from a database and recommending the target information to the target user. Therefore, various characteristics of the user and the information can be integrated by means of the first model to make decisions, and the information can be recommended more accurately while correlation between the user and the information is measured from a single dimension in the prior art is overcome.
Drawings
FIG. 1 is a flowchart of an information recommendation method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of an information recommendation device in a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an information recommendation device in a third embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
In addition, in the embodiments of the present invention, words such as "optionally" or "exemplary" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "optional" or "exemplary" is not to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of the words "optionally" or "illustratively" and the like is intended to present the relevant concepts in a concrete manner.
Example 1
The embodiment of the invention provides an information recommendation method, a specific implementation manner of which is shown in fig. 1, comprising the following steps:
s101, acquiring characteristic data of a target user.
When a user has access marks on a certain object, for example, the user browses papers on a certain website or plays movies, albums and the like on a certain playing platform, the characteristic data of the user can be obtained through a background database, and the user is the target user.
By way of example, the characteristic data may be the user's age, gender, user search terms, etc., which are not limited in this embodiment of the invention.
It should be noted that, the above manner of obtaining the user feature data may be any implementation manner in the prior art, which is not limited in the embodiment of the present invention.
S102, determining target information of a target user according to the feature data and the first model.
After the feature data of the target user is obtained, the target information of the user can be determined according to the feature data of the user and the first model.
Wherein the first model is trained from feature data extracted from the database.
Optionally, the embodiment of the invention provides an implementation manner of obtaining the first model, which is that;
and a first step of constructing a second model.
Illustratively, the second model constructed may be a depth matrix factorization (Deep Matrix Factorization, DMF) model that includes both a user network and an information network. Of course, those skilled in the art may choose other models, but the models chosen should also include two parts, namely a user network and an information network.
And secondly, generating training data according to the play log.
The training data comprises at least one user Identification (ID), at least one information ID and at least one label.
For example, historical playing data of the user may be obtained according to a playing log of the user, where the historical playing data may register an ID of a website, an ID of a playing object (i.e., information), and the like for the user, and further training data of the user may be generated based on the historical playing data.
It will be appreciated that when multiple users play multiple movies or listen to multiple albums, the play logs of the multiple users may be obtained, so as to generate training data of the multiple users and multiple information.
The tag may be used to indicate whether an access trace of a user on an object is a valid access.
And thirdly, extracting the characteristic data of the user and the characteristic data of the information from the database according to the training data.
Optionally, in order to ensure that the extracted feature data is effective data, the information with excessive playing times can be deleted by setting a threshold value, so as to avoid the phenomenon of data brushing.
For example, when the number of times a certain user plays a certain movie or a certain album exceeds a preset value, the tag corresponding to the user ID, information ID may be set to 0. Otherwise, the tag corresponding to the user ID and the information ID may be set to 1, and then the feature data of the user corresponding to the user ID and the feature data of the information corresponding to the information ID with the tag of 1 are valid data.
After the training data is acquired, feature data of the user corresponding to the user ID and feature data of the information corresponding to the information ID may be sequentially extracted from the database according to the tag in the training data.
It can be understood that the feature data of the user and the feature data of the information extracted from the database according to the tag are valid data.
The characteristic data of the user may include age, sex, search word, playlist, etc. of the user, and the characteristic data of the information may include information category, title, tag, etc.
And fourthly, processing the characteristic data of the user and the characteristic data of the information.
Optionally, the embodiment of the invention provides a processing mode that:
a. and carrying out feature processing on the feature data of the user and the feature data of the information.
For example, discrete features in the feature data of the user and the feature data of the information are subjected to one-hot (one-hot) encoding, text features in the feature data of the user and the feature data of the information are subjected to barking and word segmentation, and the text features after the barking and word segmentation are subjected to length processing.
Wherein, the discrete features can be the gender, age, category of information, etc. of the user, and the text features can be titles, labels, user search words, etc.
More specifically, the text feature after the barker word is processed in length may be matched and corresponds to a word vector in a database, for example, to Gensim in a Python library.
As is well known to those skilled in the art, gensim is a database for automatically extracting semantic topics from documents, and embodiments of the present invention are not described in detail.
After the text features are matched to obtain word vectors, average value processing is carried out on the word vectors so that the processed word vectors reach a fixed length, and the length can be a specified length in a Gensim library.
b. And splicing the characteristic data of the information subjected to the characteristic processing into a first length.
Since there may be a plurality of information extracted from the database, the plurality of information is subjected to feature processing. Therefore, the last information in the plurality of information sequentially extracted from the database can be selected, and the discrete feature and the text feature of the processed information are taken as feature data to be spliced, so that the first length is obtained.
Because the discrete features and the text features in the feature data of the information are processed to be specific lengths, the first length is the length obtained by splicing the length after the discrete features are processed and the length after the text features are processed.
It will be appreciated that if there is only one information extracted from the database, the last information selected is the information currently extracted.
c. And splicing the characteristic data of the user subjected to the characteristic processing into a second length.
When there are a plurality of pieces of information extracted from the database, the rest information except the last piece of information can be selected, and the feature data corresponding to each rest information ID (namely, the feature data of the rest information) is input into the second model to obtain the feature vector value of the rest information;
specifically, feature data corresponding to the remaining information ID may be input into the information network of the second model to obtain a feature vector value of the remaining information.
And carrying out average value calculation on the feature vector values of the rest information to obtain the average value of the feature vector values of the rest information.
It will be appreciated that when there is only one information extracted from the database, then the average of the feature vector values of the remaining information is 0.
And splicing the average value of the characteristic vector values of the rest information with the characteristic data of the user after the characteristic processing to obtain a second length.
Since the feature vector of the remaining information is output via the second model, the length of the feature vector of the remaining information is correlated with the second model, and can be set to a fixed length. Similarly, the length of the average value of the feature vectors of the remaining information is also a fixed length.
And, because the discrete feature and text feature in the feature data of the user are both of a specific length after being processed. Therefore, the second length is that the length of the average value of the feature vector values of the rest information is the length obtained by splicing the length of the processed discrete feature with the length of the text feature in the feature data of the user.
And fifthly, training a second model according to the processed characteristic data of the user and the characteristic data of the information.
After the fourth step of processing, the characteristic data of the information with the first length and the characteristic data of the user with the second length obtained after the splicing are used as input data and are respectively input into an information network and a user network of the second model, and the second model is trained.
And sixthly, determining the trained second model as the first model.
In the training process, the model parameters can be set by those skilled in the art according to actual requirements. Such as the number of users tested, the number of samples entered, etc.
And determining the trained second model as the first model when the second model is trained to meet the expected requirement.
S103, recommending the target information to the target user.
Target information of the user, for example, a certain movie the user likes to watch, a certain album the user likes to listen to, etc., is determined based on the trained first model.
And recommending the target information to the user.
The embodiment of the invention provides an information recommendation method, which is used for determining target information of a target user through acquired characteristic data of the target user and a first model obtained through training of the characteristic data extracted from a database and recommending the target information to the target user. Therefore, various characteristics of the user and the information can be integrated by means of the first model to make decisions, and the information can be recommended more accurately while correlation between the user and the information is measured from a single dimension in the prior art is overcome.
Example two
The embodiment of the invention provides an information recommending device, as shown in fig. 2, which comprises: an acquisition module 201, a determination module 202 and a recommendation module 203.
The acquiring module 201 is configured to acquire feature data of a target user;
a determining module 202, configured to determine target information of a target user according to the feature data and a first model, where the first model is obtained by training according to the feature data extracted from the database;
and the recommending module 203 is used for recommending the target information to the target user.
Further, the training of the feature data extracted from the database to obtain the first model includes:
constructing a second model, wherein the second model can be a model comprising a user network and an information network;
generating training data according to the play log, wherein the training data comprises at least one user ID, at least one information ID and at least one label, and the label is used for indicating whether an access trace of a certain user on a certain object is effectively accessed;
extracting feature data of the user and feature data of information from a database according to the training data;
processing the characteristic data of the user and the characteristic data of the information;
training a second model according to the processed characteristic data of the user and the characteristic data of the information;
determining the trained second model as the first model;
wherein, the extracting the characteristic data of the user and the characteristic data of the information from the database according to the training data comprises:
feature data of a user corresponding to the at least one user ID and feature data of information corresponding to the at least one information ID are sequentially extracted from the database according to the at least one tag.
It is understood that the feature data of the user and the feature data of the information extracted from the database based on the tag are both valid data.
Further, processing the characteristic data of the user and the characteristic data of the information includes:
carrying out feature processing on the feature data of the user and the feature data of the information;
splicing the characteristic data of the information subjected to the characteristic processing into a first length;
and splicing the characteristic data of the user subjected to the characteristic processing into a second length.
The feature processing of the feature data of the user and the feature data of the information may include:
performing single-heat coding on discrete features in the feature data of the user and the feature data of the information;
and performing the barking and word segmentation on text features in the feature data of the user and the feature data of the information, and performing length processing.
For example, the length process described above may be matching text features that are bargain segmented to word vectors in a corresponding database, e.g., to Gensim in a Python library.
Splicing the feature data of the information after feature processing into a first length may include:
and splicing the characteristic data of the last information in the at least one information sequentially extracted from the database after the characteristic processing into a first length.
Splicing the feature data of the user after the feature processing into a second length may include:
inputting the characteristic data of each piece of information corresponding to the information ID of the rest of information into the information network of the second model to obtain the characteristic vector value of the rest of information;
calculating the average value of the feature vector values of the rest information to obtain the average value of the feature vector values of the rest information;
and splicing the average value of the feature vector values of the rest information and the feature data of the user after the feature processing into a second length.
The information recommending device provided by the embodiment of the invention can execute the information recommending method provided by the first embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example III
Fig. 3 is a schematic structural diagram of an information recommendation device provided in embodiment 3 of the present invention, and as shown in fig. 3, the device includes a processor 301, a memory 302, an input device 303 and an output device 304; the number of processors 301 in the device may be one or more, one processor 301 being taken as an example in fig. 3; the processor 301, memory 302, input device 303 and output device 304 in the apparatus may be connected by a bus or other means, in fig. 3 by way of example.
The memory 302 is used as a computer readable storage medium, and may be used to store a software program, a computer executable program, and a module, such as program instructions/modules corresponding to the information recommendation method in the first embodiment of the present invention (for example, the acquisition module 201, the determination module 202, and the recommendation module 203 in the information recommendation device). The processor 301 executes various functional applications of the device and data processing, i.e., implements the information recommendation method described above, by running software programs, instructions, and modules stored in the memory 302.
Memory 302 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 302 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 non-volatile solid-state storage device. In some examples, memory 302 may further include memory located remotely from processor 301, which may be connected to the device/terminal/server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 303 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output device 304 may include a display device such as a display screen.
Example IV
A fourth embodiment of the present invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing an information recommendation method, the method comprising:
acquiring characteristic data of a target user;
determining target information of a target user according to the feature data and the first model;
wherein the first model is obtained by training based on the feature data extracted from the database;
and recommending the target information to the target user.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the method operations described above, and may also perform the related operations in the information recommendation method provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the information recommending apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (6)
1. An information recommendation method, comprising:
acquiring characteristic data of a target user;
determining target information of the target user according to the characteristic data and a first model;
wherein the first model is obtained by training according to the feature data extracted from the database;
recommending the target information to the target user;
extracting feature data from a database for training to obtain the first model, comprising:
constructing a second model;
generating training data according to the play log;
extracting feature data of the user and feature data of information from the database according to the training data;
processing the characteristic data of the user and the characteristic data of the information;
training the second model according to the processed characteristic data of the user and the characteristic data of the information;
the characteristic data of the information with the first length and the characteristic data of the user with the second length obtained after the splicing are used as input data and are respectively input into an information network and a user network of the second model, and the second model is trained;
determining a trained second model as the first model;
the processing the characteristic data of the user and the characteristic data of the information comprises the following steps:
carrying out feature processing on the feature data of the user and the feature data of the information;
splicing the characteristic data of the information subjected to the characteristic processing into the first length;
splicing the characteristic data of the user after the characteristic processing into the second length;
the splicing the characteristic data of the information after the characteristic processing into the first length includes:
splicing the discrete features and text features, which are obtained by carrying out feature processing on last information in at least one piece of information sequentially extracted from the database, as feature data to form the first length; the first length is obtained by splicing the processed discrete feature length and the processed text feature length;
the splicing the feature data of the user after the feature processing into the second length includes:
splicing the average value of the feature vector values of the rest information and the feature data of the user after feature processing to obtain a second length, wherein the second length is obtained by splicing the length of the average value of the feature vector values of the rest information and the length of the discrete feature after the processing and the length of the text feature in the feature data of the user; wherein the remaining information is all information except the last information in at least one information extracted from the database.
2. The information recommendation method according to claim 1, wherein the training data includes at least one user ID, at least one information ID, and at least one tag;
the extracting the characteristic data of the user and the characteristic data of the information from the database according to the training data comprises the following steps:
and sequentially extracting the characteristic data of the user corresponding to the at least one user ID and the characteristic data of the information corresponding to the at least one information ID from the database according to the at least one tag.
3. The information recommendation method according to claim 1, wherein the feature processing of the feature data of the user and the feature data of the information includes:
performing single-heat coding on discrete features in the feature data of the user and the feature data of the information;
and performing the resultant segmentation on the text features in the feature data of the user and the feature data of the information, and performing length processing.
4. The information recommendation method according to claim 1, wherein before obtaining an average value of feature vector values of the remaining information, the method further comprises:
inputting the characteristic data of each piece of information corresponding to the information ID of the rest of information into the second model to obtain the characteristic vector value of the rest of information;
and calculating the feature vector values of the rest information to obtain an average value of the feature vector values of the rest information.
5. An information recommendation device, characterized by comprising:
the acquisition module is used for acquiring the characteristic data of the target user;
the determining module is used for determining target information of a target user according to the characteristic data and the first model;
wherein the first model is obtained by training according to the feature data extracted from the database;
the recommending module is used for recommending the target information to the target user;
the recommendation module is also used for constructing a second model;
generating training data according to the play log; extracting feature data of the user and feature data of information from a database according to the training data; processing the characteristic data of the user and the characteristic data of the information; training a second model according to the processed characteristic data of the user and the characteristic data of the information; the characteristic data of the information with the first length and the characteristic data of the user with the second length obtained after the splicing are used as input data and are respectively input into an information network and a user network of the second model, and the second model is trained; determining the trained second model as the first model;
the processing the characteristic data of the user and the characteristic data of the information comprises the following steps:
carrying out feature processing on the feature data of the user and the feature data of the information;
splicing the characteristic data of the information subjected to the characteristic processing into the first length;
splicing the characteristic data of the user after the characteristic processing into the second length;
the splicing the characteristic data of the information after the characteristic processing into the first length includes:
splicing the discrete features and text features, which are obtained by carrying out feature processing on last information in at least one piece of information sequentially extracted from the database, as feature data to form the first length; the first length is obtained by splicing the processed discrete feature length and the processed text feature length;
the splicing the feature data of the user after the feature processing into the second length includes:
splicing the average value of the feature vector values of the rest information and the feature data of the user after feature processing into the second length; the second length is obtained by splicing the length of the average value of the feature vector values of the rest information and the length of the processed discrete feature with the length of the text feature in the feature data of the user; wherein the remaining information is all information except the last information in at least one information extracted from the database.
6. An information recommendation device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the information recommendation method according to any of claims 1-4 when executing the computer program.
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| CN110413637A (en) | 2019-11-05 |
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Address after: Room 2062, Building No. 588 Zixing Road, Minhang District, Shanghai, 201203 Applicant after: Shanghai Himalaya Technology Co.,Ltd. Address before: Room 2062, Building No. 588 Zixing Road, Minhang District, Shanghai, 201203 Applicant before: SHANGHAI ZHENGDA XIMALAYA NETWORK TECHNOLOGY CO.,LTD. |
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