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Data mining based hybrid filtering film recommendation method

A technology of data mining and recommendation method, applied in the field of recommendation, which can solve the problems of sparse number of users and insufficient recommendation accuracy.

Inactive Publication Date: 2016-12-14
WUHAN UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the defects of the above-mentioned background technology, the present invention provides a hybrid movie recommendation method based on data mining to solve the problem of insufficient recommendation accuracy caused by the sparse number of users

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  • Data mining based hybrid filtering film recommendation method
  • Data mining based hybrid filtering film recommendation method
  • Data mining based hybrid filtering film recommendation method

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0046] A hybrid filtering movie recommendation method based on data mining, including:

[0047] Step 1, establish a movie attribute matrix M and a preliminary user rating matrix G;

[0048] Step 11, set up a collection of movies M={m 1 ,m 2 , m 3 ,...,m n}, the feature vector of each movie is where w jl represents each feature vector for movie m j The weight of the feature vector p k It is the attribute information possessed by the movie, such as the region of the movie, the genre (such as comedy, horror, action, etc.), the information of the director and actors, and the like. Among them, l represents the length of the vector, and j and k respectively represent the serial number marks of the movie and movie attributes. After the establishment, the movie collection M={m 1 ,m 2 ,m 3 ,...,m n}.

[0049] In addition L={l 1 , l 2 , l 3 ,..., ...

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Abstract

The invention discloses a data mining based hybrid filtering film recommendation method. The data mining based hybrid filtering film recommendation method comprises the steps that firstly, a film attribute matrix M and a preliminary user score matrix G are established; then film key words daily browsed by a user are mined, then user clustering is performed by utilizing an ant colony method, user score which is empty in the preliminary user score matrix G is simulated, the sparsity problem of the user score matrix G is solved, and a perfect user score matrix G is obtained; a user interesting matrix H is obtained according to the perfect user score matrix G and the film attribute matrix M; finally, the user interesting matrix H is combined with the film attribute matrix M to obtain a recommended film score matrix Q. The clustering effect is improved by utilizing mined data, meanwhile the accuracy and stability of the ant colony method are improved, and the weight of a user interesting vector in the user interesting migration problem is optimized.

Description

technical field [0001] The method relates to the field of recommendation methods, in particular to a hybrid filtering recommendation method based on data mining. Background technique [0002] With the rapid development of the Internet, it is becoming more and more difficult to find the information you want in this era of information explosion. At the same time, people have the option to buy their preferred movie tickets before watching movies, which has also become a problem that plagues many people. When users choose movies that are most likely to meet their own tastes for going to the cinema, users urgently need help from recommended content that can actually meet their own preferences and needs, so the research on movie recommendation methods is imperative. [0003] The recommendation method actively pushes information or services that the user may like to the user, which can help the user obtain more useful information and greatly reduce the time spent. The traditional...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62H04L29/08
CPCG06F16/9535G06F2216/03H04L67/55G06F18/23
Inventor 肖攸安孔令炜吴杨君
Owner WUHAN UNIV OF TECH
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