Partial model weight fusion Top-N film recommending method based on user clustering

A user clustering and local model technology, applied in computer components, electrical digital data processing, character and pattern recognition, etc., can solve problems such as single training data and inability to accurately capture user preferences

Active Publication Date: 2018-08-03
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] In order to overcome the problem that a single model in the prior art cannot accurately capture user preferences and the multi-model fusion algorithm uses a single training data, the present invention provides a new local model weighted fusion movie recommendation algorithm based on user clustering to realize the Top- N personalized recommendation

Method used

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  • Partial model weight fusion Top-N film recommending method based on user clustering
  • Partial model weight fusion Top-N film recommending method based on user clustering
  • Partial model weight fusion Top-N film recommending method based on user clustering

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Embodiment Construction

[0049] refer to figure 1 The general flow chart of the technical solution, the present invention has four stages, namely: data preprocessing stage, user clustering stage, global recommendation model and local recommendation model training stage, and recommendation model linear weighted fusion stage. The data preprocessing stage is to clean the data set, remove some inactive users and unpopular movies, construct a corpus for LDA topic model training and a user movie implicit feedback training matrix for sparse linear model training; user clustering stage , use the user corpus obtained in the first stage to train the LDA topic model to obtain the user feature vector, and realize the clustering of users through the spectral clustering algorithm, and each cluster generates a local implicit feedback training matrix; the global recommendation model and the local In the recommendation model training stage, the original implicit feedback matrix and the local implicit feedback matrix a...

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Abstract

The invention discloses a partial model weight fusion Top-N film recommending method based on user clustering. The method comprises the steps of 1, preprocessing data, wherein inactive users and filmswith very low popularity are subjected to data cleaning, user film label documents are constructed, explicit scoring information is converted into implicit feedback information, and a user-film implicit feedback matrix A is constructed; 2, conducting user clustering, wherein film label information is utilized, user feature vectors are obtained by training an LDA topic model, and user clustering is achieved through a spectral clustering algorithm; 3, determining a local recommending model and training a global recommending model; 4, conducting model weight fusion recommending; 5, proving the effectiveness of the models through a leave-one-out method.

Description

technical field [0001] The invention relates to a method for recommending movies on the network. Background technique [0002] With the rapid development of information technology and social networks, the data generated by the Internet has recently skyrocketed, and the era of big data is coming. With the increase of the amount of data, it becomes more and more difficult for people to find the information they really want from the massive data. At this time, the recommendation system can exert its maximum application value. Based on user profiles, item information, and user historical behavior data, the recommendation algorithm can accurately predict user preferences and recommend items that may be of interest to users in a personalized manner, greatly reducing the cost for users to discover target information. [0003] Recommendation algorithms can be divided into content-based recommendation and collaborative filtering recommendation. Modern recommendation systems mainly...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/735G06F18/23
Inventor 汤颖孙康高
Owner ZHEJIANG UNIV OF TECH
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