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Method and system for accurate recommendation of TV products based on explicit and implicit latent factor model

A technology of factor model and recommendation method, applied in the field of recommendation, can solve the problems of unable to recommend users, limited scope of use, unexplainable, etc.

Active Publication Date: 2020-12-15
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The content-based recommendation algorithm is to directly analyze the product content, and recommend products with similar content according to the past preferences of the target users. This recommendation algorithm is simple and direct, but the scope of use is limited, and it is only used for products with existing prominent labels.
The problem with this type of algorithm is that it cannot recommend products in categories that users have never touched
However, in the LFM model, the decomposed features cannot be explained, and these features are often obtained through mathematical calculations rather than artificially specified

Method used

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  • Method and system for accurate recommendation of TV products based on explicit and implicit latent factor model
  • Method and system for accurate recommendation of TV products based on explicit and implicit latent factor model
  • Method and system for accurate recommendation of TV products based on explicit and implicit latent factor model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0081] like figure 1 As shown, an accurate recommendation method for TV products based on the explicit and implicit latent factor model includes the following steps:

[0082] Step S101: The title of the TV product is processed through regular expressions, comprehensively considering multiple anti-crawler mechanisms, designing a crawler strategy, and crawling the required external data;

[0083] Specifically, the step S101 includes:

[0084] Step S1011: designing an anti-crawler mechanism, the anti-crawler mechanism includes actively initiating an asynchronous request to obtain required data by simulating an Ajax request;

[0085] Step S1012: Design a web crawler algorithm according to the anti-crawler mechanism, and crawl webpage data:

[0086] Take the anti-crawler mechanism to continuously initiate Http requests, then receive Http responses, parse the obtained HTML file, and if it is a definite structure, directly match the data in the label;

[0087] If the structure is ...

Embodiment 2

[0131] like figure 2 As shown, another accurate recommendation method for TV products based on the explicit and implicit latent factor model includes the following steps:

[0132] Step S201: Process the proper title of the TV product through regular expressions, comprehensively consider various anti-crawler mechanisms, design a crawler strategy, and crawl the required external data; the proper title of the TV product includes the title of the TV series, the number of episodes, and the name of the variety show TV program titles such as "Peacekeeping Infantry Battalion (19)", "October 19th Nature: A Bird's Eye View of the Earth (05)", etc., the title of the TV product can be obtained from the TV product information, and the main content of the TV product information is It consists of logo, TV product proper title, creation date, director, actor, production year, content description, total episodes, category name, series category, channel language, and regional parameters. The T...

Embodiment 3

[0270] like Figure 8 As shown, a precise recommendation system for TV products based on the explicit and implicit latent factor model, including:

[0271] The automatic labeling module 301 is used to process the proper title of TV products through regular expressions, comprehensively consider multiple anti-crawler mechanisms, design crawler strategies, and crawl the required external data;

[0272] The automatic tagging module 302 is used to establish classification models for TV products and user groups respectively according to the different characteristics of the TV products and user groups, and realize automatic tagging of TV product information and user information through the classification model, and obtain the tagged Labeled TV product information and labeled user information;

[0273] The explicit latent factor model construction module 303 is used to obtain the explicit latent factor according to the TV product information data label table, the user rating informat...

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Abstract

The present invention relates to the technical field of recommendation, and discloses an accurate recommendation method for television products based on an explicit and implicit latent factor model, including: processing the title of the television product through a regular expression, designing a crawler strategy, and crawling required external data; According to the different characteristics of TV products and user groups, establish classification models for TV products and users, so as to realize automatic labeling of different TV products and users in the crawled external data, and obtain the labeled TV product information and labeling labels The final user information; thus, the explicit latent factors are obtained, and the implicit latent factors are obtained according to the explicit latent factors, and the explicit latent latent factor model is constructed based on the explicit latent factors and the latent latent factors; Factor model for TV product recommendation. The invention also discloses an accurate recommendation system for television products based on an explicit and implicit latent factor model. The invention improves the accuracy of recommendation.

Description

technical field [0001] The invention relates to the technical field of recommendation, in particular to a method and system for accurately recommending television products based on an explicit and implicit latent factor model. Background technique [0002] With the rapid development of the Internet, information presents an explosive growth trend, and countless information floods into thousands of households every day. The "three-network integration" under this situation has brought opportunities for the development of traditional radio and television media. Radio and television operators can obtain useful information from each user's historical information and real-time interactive information, but find users from a large amount of information. Information of interest is very difficult. In order to solve this problem, the recommendation system mines the user's preferences by analyzing the user's relevant data, including personal social attributes, browsing logs, etc., so as...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N21/258H04N21/25H04N21/845H04N21/81H04L29/08G06K9/62
CPCH04N21/25891H04N21/25866H04N21/251H04N21/252H04N21/8455H04N21/8133H04L67/02G06F18/2411
Inventor 奚琪桂智杰李创项永明杨萍
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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