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Movie recommendation method based on prospect theory and multi-objective evolution

A technology of multi-objective evolution and recommendation method, applied in the field of multi-objective optimization algorithm and recommendation algorithm, which can solve the problem of not considering human behavior characteristics.

Active Publication Date: 2021-03-30
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, these algorithms do not consider human behavior characteristics in the recommendation process. Therefore, to solve this problem, the present invention proposes a movie recommendation algorithm based on prospect theory and multi-objective evolution. The algorithm uses an improved collaborative filtering algorithm to calculate the accuracy target , and use the prospect theory to select the population in the evolution process, and verify the effect of the algorithm by comparing with other recommendation algorithms

Method used

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  • Movie recommendation method based on prospect theory and multi-objective evolution
  • Movie recommendation method based on prospect theory and multi-objective evolution
  • Movie recommendation method based on prospect theory and multi-objective evolution

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Embodiment Construction

[0048]The present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0049] The present invention uses Movielens as a data set for movie recommendation. This data set includes information on 943 users, information on 1682 movies, and ratings from 100,000 users on movies. EPMOEA and the multi-objective evolutionary recommendation algorithm MOEA before improvement and the traditional The recommendation method is based on user-based collaborative filtering algorithm (UserCF), item-based recommendation algorithm (ItemCF), and bipartite graph-based recommendation algorithm (Probs) for experimental comparison.

[0050] In the two multi-objective optimization algorithms MOEA and EPMOEA, 10 and 20 movies are recommended for each user, the running algebra gen=400, and the population size is set to pop size = 50, crossover probability p c =0.8, mutation probability p m =0.4.

[0051] The performance evaluation function of ...

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Abstract

The invention discloses a movie recommendation method based on a foreground theory and multi-objective evolutionary, and provides an improved multi-objective evolutionary recommendation algorithm considering that different users experience different choices according to their psychologies when making decisions and the psychological behaviors of the users are not considered in previous multi-objective evolutionary recommendation. Firstly, precision measurement based on the foreground theory is provided, and then the variation process of population selection and individual multi-similar users based on finite rationality is provided in the evolution process. Experimental results show that compared with a traditional recommendation algorithm, the EPMOEA can achieve good balance between precision and diversity.

Description

technical field [0001] The invention belongs to the technical field of multi-objective optimization algorithm and recommendation algorithm. Using improved multi-objective optimization algorithm (specifically involving foreground selection and multi-similar user mutation) and improved user-based collaborative filtering algorithm for movie recommendation, hoping to improve the performance of movie recommendation. Background technique [0002] With the development of the Internet, people can collect more and more information every day, and to quickly find the information they are interested in from a large amount of data, the recommendation system is particularly important. The recommendation system mainly uses the recommendation algorithm to predict the information that the user may be interested in based on the user's previous information, thereby generating recommendations. It avoids the cost of users spending a lot of time inquiring about information, and improves the effi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/735G06F16/9535G06F16/9536
CPCG06F16/735G06F16/9535G06F16/9536
Inventor 杨新武陈晓丹
Owner BEIJING UNIV OF TECH
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