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Film recommending method and system based on visual features

A technology of visual features and recommendation methods, applied in the computer field, can solve problems such as improvement, and the accuracy of recommendation cannot be guaranteed.

Active Publication Date: 2016-11-30
GUANGZHOU HKUST FOK YING TUNG RES INST
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In fact, this assumption only imposes certain restrictions on the learning process of the factor matrix
Therefore, although the existing context-based matrix factorization method can improve the recommendation effect and performance to a certain extent, it cannot guarantee the improvement of recommendation accuracy.

Method used

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  • Film recommending method and system based on visual features
  • Film recommending method and system based on visual features
  • Film recommending method and system based on visual features

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Embodiment Construction

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0066] See figure 1 , Is a schematic flow chart of an embodiment of a method for recommending movies based on visual features provided by the present invention, including steps S1 to S4, which are specifically as follows:

[0067] S1. Obtain pre-trained characteristic factors of the user to be recommended;

[0068] S2. Obtain the pre-trained feature factor of the unrated movie of the user to be recommended, the similarity coefficient betwee...

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Abstract

The present invention discloses a film recommending method based on visual features. The method comprises: predicating predication scores of films without scoring through users to be recommended through adoption of a film predication model built in advance according to the feature factors of the users to be recommended, the feature factors of the films without scoring, the similarity coefficient between the films without scoring and other films and the visual features of the films without scoring extracted in advance and the weight of each feature in the visual features, wherein the visual features includes a color histogram, SIFT features, CNN features and film category features; and determining whether the films without scoring are recommended to the users to be recommended or not according to the predication scores. The present invention further discloses a film recommending system based on the visual features. According to the embodiment of the invention, the accuracy of film recommending can be improved, and the user experience can be enhanced.

Description

Technical field [0001] The invention relates to the field of computer technology, in particular to a method and system for recommending movies based on visual features. Background technique [0002] Movie recommendation system is one of the popular recommendation system forms today. The working principle of movie recommendation is to predict the movies that the user may like by analyzing the user's historical movie viewing behavior. However, because the user's historical behavior data is too small, the recommendation effect is very poor. This phenomenon is called the data sparse problem. Among current recommendation algorithms, matrix factorization is one of the most effective methods that have been proven. According to the difference of the data used, the matrix factorization technology in the recommendation algorithm is divided into matrix factorization without context and matrix factorization based on context. [0003] The matrix factorization method without context can be de...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/22G06F18/214
Inventor 赵莉莉吕仲琪杨强
Owner GUANGZHOU HKUST FOK YING TUNG RES INST
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