A Matrix Factorization Recommendation Method Based on Joint Clustering

A technology of matrix decomposition and recommendation method, applied in text database clustering/classification, instrument, unstructured text data retrieval, etc., can solve the problem of low time efficiency of collaborative filtering algorithm

Active Publication Date: 2019-09-10
成都视海芯图微电子有限公司
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Problems solved by technology

[0005] In order to solve the problem of low timeliness of the existing collaborative filtering algorithm, the present invention proposes a matrix decomposition recommendation method based on joint clustering, in order to make full use of the close correlation between clusters and the high precision of the probability matrix decomposition algorithm , for big data processing problems in the era of information overload, it can recommend at a faster speed while ensuring good accuracy

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  • A Matrix Factorization Recommendation Method Based on Joint Clustering
  • A Matrix Factorization Recommendation Method Based on Joint Clustering
  • A Matrix Factorization Recommendation Method Based on Joint Clustering

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Embodiment

[0105] In order to verify the effect of the method in this patent, the operating environment of the experiment is first set up: Intel Core i5CPU, 3.00GHZ main frequency, Windows10 system, 12G memory. This article selects the MovieLens 10M data set commonly used in the recommendation system. For each tag in the data set, it is deleted if there are less than 5 different users and movies; for each different user and movie, less than 5 Different tags are also removed.

[0106] In this paper, the root mean square error (RMSE) is used as the evaluation criterion.

[0107] This paper selects four methods to compare the effects with the methods proposed in this paper, namely probability matrix decomposition (PMF), label-based probability matrix decomposition (NHPMF), joint clustering algorithm (Co-Clustering) and Co-Clustering+ There are four types of PMF. Specifically, according to the experimental results, the results can be drawn as shown in Table 1:

[0108] Table 1 RMSE values...

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Abstract

The invention discloses a matrix decomposition recommendation method based on joint clustering. The matrix decomposition recommendation method includes the steps that a user-item rating matrix is constructed; the user-item rating matrix is divided into multiple categories through joint clustering; for the categories obtained after clustering, a probability matrix decomposition method is used for parallely predicting unknown scores of all the categories, and recommendation is carried out according to the predicting scores. Close correlation in the clustering and high precision of the probability matrix decomposition algorithm can be used fully, and for the big data processing problem in the information overload times, recommendation can be conducted at high speed while high precision is ensured.

Description

technical field [0001] The invention relates to the field of personalized recommendation, in particular to a matrix decomposition recommendation method based on joint clustering. Background technique [0002] With the development of network technology, users lose their direction in the mass of information, and it is difficult to select the information they really need, which reduces the efficiency of information use. This is the so-called information overload problem. In order to solve the problem of information overload, recommendation system came into being. The recommendation system is a process of recommending items of interest to users according to their information needs, which is a personalized recommendation process. The personalized recommendation system is positioned on the interest of the target user, does not require the user to input keywords, and actively recommends some things that the user may be interested in from the user's previous behaviors, such as brow...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/9536G06F16/35G06F17/16
Inventor 刘学亮杨文娟吴乐汪萌洪日昌
Owner 成都视海芯图微电子有限公司
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