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Personalized movie similarity calculation method based on user interest model

A technology of similarity calculation and interest model, which is applied in the field of personalized movie similarity calculation for new users, which can solve the problems of user satisfaction, lack of personalized characteristics, and failure to consider the influence of different users' movies.

Active Publication Date: 2015-03-25
SHANDONG UNIV OF SCI & TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional methods of calculating movie similarity are only related to the content characteristics of the movie itself, without considering the influence of different users' interest preferences on movies
According to the existing calculation method, the personalized characteristics cannot be reflected, and such user experience is difficult to obtain user satisfaction.

Method used

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  • Personalized movie similarity calculation method based on user interest model
  • Personalized movie similarity calculation method based on user interest model
  • Personalized movie similarity calculation method based on user interest model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] to combine figure 1 As shown, a method for calculating the similarity of movie personalization based on the old user interest model, including steps:

[0048] s1. Collection of user interests

[0049] Select the user behavior data within a certain period of time T and the highest rated N movies in the viewing records during this period of time, and establish a user dynamic behavior information database;

[0050] Here, the user behavior data mainly refers to the user's dynamic interests, including clicking, searching, watching, and collecting behaviors.

[0051] s2. Formal representation of user interest model

[0052] Through the analysis of the special media such as movies, the present invention uses a two-layer six-dimensional space vector to represent the user interest model, and the two-layer six-dimensional space vector is as follows: figure 2 shown.

[0053] The user's movie interest model includes two layers, namely: the user's preference for each dimension ...

Embodiment 2

[0087] In Embodiment 2, for new users, since the user has no historical behavior, at this time, according to the user's registration information, it includes explicit information such as age and gender, as well as the interest preferences of the public of the same age and gender. Based on the content characteristics of the movie itself, the method of weighted summation of each dimension is used to calculate the similarity of the movie. The specific process is shown as follows image 3 shown.

[0088] A method for calculating the personalized similarity of movies for new users, comprising the following steps:

[0089] s1. Extract the actor information, director information, genre information, region information, time information and content brief information of each movie to form a six-dimensional vector space, and calculate the similarity value x of each dimension offline i ;

[0090] s2. Based on the user's explicit information, classify the user, find the cluster that is m...

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Abstract

The invention discloses a personalized movie similarity calculation method based on a user interest model. The method includes: according to users' historical behaviors, namely through a personalized movie recommendation system platform, mining users' various behaviors of searching a movie resource library and users' watching and collecting behaviors; fully mining and analyzing different preference degrees of different users, upon six basic attributes including performers, directors, types, regions, times and content introductions of movies, thus acquiring a first-layer six-dimensional spatial vector representation of a user model; according to the users' behaviors above, by means of keyword extraction or semantic analysis, analyzing the different users' weights of characteristic values in the six dimensions, thus acquiring a second-layer six-dimensional spatial vector representation of the user model; using a two-layer multi-dimensional spatial vector to represent the user interest model, generating different movie similarity lists for the different users on the basis of the user interest model and basic content characteristics of movies. Therefore, recommending is more effective.

Description

technical field [0001] The invention relates to a method for calculating the personalized similarity of movies based on an old user's interest model, and a method for calculating the personalized similarity of movies for new users. Background technique [0002] With the rapid development of the Internet, in the face of increasingly updated massive movie resources, personalized recommendation applications are randomly generated. [0003] Currently, the main popular recommendation algorithms are: association rule-based recommendation, knowledge-based recommendation, content-based recommendation, collaborative filtering recommendation and combination recommendation, etc. The above recommendation algorithms all involve a key technology, namely: calculating the similarity between items, and finding the nearest neighbors of the items according to the similarity. The traditional methods of calculating movie similarity are only related to the content characteristics of the movie it...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 赵建立张春升吴文敏孟芳
Owner SHANDONG UNIV OF SCI & TECH
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