Time-sensitive personalized recommendation method based on probability matrix decomposition

A probabilistic matrix decomposition, time-sensitive technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of insufficient use of movie categories, neglect of context information, etc., to alleviate the problem of sparsity, use reasonable, improve The effect of precision

Active Publication Date: 2018-11-06
TIANJIN UNIVERSITY OF TECHNOLOGY
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problems of ignoring contextual information and insufficient use of movie categories when making recommendations in existing recommendation systems, and proposes a method based on probability matrix decomposition that makes full use of contextual information and movie categories to make more accurate recommendations. Time Sensitive Personalized Recommendation Method

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  • Time-sensitive personalized recommendation method based on probability matrix decomposition
  • Time-sensitive personalized recommendation method based on probability matrix decomposition
  • Time-sensitive personalized recommendation method based on probability matrix decomposition

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Embodiment 1

[0027] The time-sensitive personalized recommendation method based on probability matrix decomposition provided by the present invention introduces time context in addition to the traditional user-item scoring matrix to construct a brand new user-situation scoring matrix. See attached figure 1 , the specific construction process of the present invention is as follows:

[0028] step one:

[0029] 1) Split the original movie watched by a user according to category: In fact, a movie may involve multiple movie categories, that is, a movie is usually classified as a combination of multiple categories, such as movie Batman belongs to the action, crime, and adventure genres at the same time. Table 1 is user u iThe rating data from the movie dataset MovieLens represents the user u i The rating for a specific movie on a given day of the week. We first split each movie category combination in Table 1 into corresponding individual categories, and the user's ratings for individual mo...

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Abstract

A time-sensitive personalized recommendation method based on probability matrix decomposition belongs to the field of context-aware personalized recommendation, comprising: constructing a user-contextscoring matrix by using the user's rating information and context information of the movie and the movie category: constructing a user-context scoring matrix according to the original user-movie scoring matrix and additional context information and movie category information, performing matrix decomposition then to obtain a user feature vector containing the context information, then solving thecosine similarity between the two users, selecting a certain number of users with higher similarity as the neighbor users, and integrating the influence of the neighbor users into the probability matrix decomposition for scoring prediction, thereby making personalized recommendations based on the level of the rating forecast. The invention is applicable to the fields of movie recommendation, e-commerce website products, digital library book recommendation, Internet advertisement placement and the like which require personalized recommendation.

Description

technical field [0001] The invention belongs to the field of context-aware personalized recommendation. A time-sensitive personalized recommendation method based on probability matrix factorization is proposed. Background technique [0002] A class of applications, personalized recommendation systems are widely used in many fields, such as e-commerce websites, digital libraries, travel services, Internet advertising and so on. With the advent of the era of big data, information on the Internet has shown explosive growth, and the problem of information overload will inevitably follow. When users intend to find the items they are interested in, they will encounter a lot of trouble, so how to help users obtain the items they are interested in, so that some unknown and unpopular items that are of great value to users can be obtained from a large amount of data It has become a key research field when it is excavated in , and recommendation system is one of the most effective me...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 肖迎元王高伟郑文广
Owner TIANJIN UNIVERSITY OF TECHNOLOGY
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