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Recommendation system optimization method with information of user and item and context attribute integrated

A technology of attribute information and recommendation system, applied in the field of matrix decomposition model, which can solve the problem that the attribute information of users and items is not fully utilized, etc.

Active Publication Date: 2013-03-20
珠海市颢腾智胜科技有限公司
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Problems solved by technology

[0004] In the existing improved methods based on matrix factorization model, user, item attribute information is not fully utilized, and few methods combine user, item, and context attribute information simultaneously to matrix factorization model to improve recommendation accuracy

Method used

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  • Recommendation system optimization method with information of user and item and context attribute integrated
  • Recommendation system optimization method with information of user and item and context attribute integrated
  • Recommendation system optimization method with information of user and item and context attribute integrated

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Embodiment Construction

[0021] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0022] A recommender system optimization method that integrates user, item, and context attribute information simultaneously integrates user, item, and context attribute information into the matrix factorization model to correct the deviation of prediction scores and improve the recommendation accuracy of personalized recommendation systems.

[0023] The matrix decomposition model described considers the potential relationship between users and items, and introduces the global average score μ, user u’s rating bias item b u and item i's score deviation item b i , get user u's predicted rating for item i:

[0024] r ^ u , i = μ + b u + b ...

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Abstract

The invention discloses a recommendation system optimization method with information of a user and an item and a context attribute integrated. According to the method, the information of the user, the item and the context attribute is integrated in a matrix decomposition model, and recommendation accuracy is improved in a personalized recommendation system. The recommendation system optimization method with the information of the user, the item and the context attribute integrated is characterized in that different influences of the information of the user, the item and the context attribute on overall scores, user interests and item scores are considered, and is applied to calculation of an original matrix decomposition model. The influences of the user, the item and the context attribute on the scores are considered at the same time, and therefore the recommendation accuracy is obviously higher than that of the rectangular decomposition model which only adopts user program two-dimensional score matrix information.

Description

technical field [0001] The present invention relates to a recommendation system optimization method that integrates user, item and context attribute information, and specifically relates to a method that considers the impact of user, item and context attribute information on overall score, user score and item score, and integrates it into a matrix decomposition model, thereby The method for improving the recommendation accuracy of a recommendation system is applicable to a collaborative filtering recommendation system and belongs to the technical field of recommendation system research. Background technique [0002] The purpose of the recommendation system is to fully tap the interests of users and help users find what they are interested in. In the past two decades, recommender systems have been extensively studied and successfully applied to various Internet commercial systems. However, how to generate more accurate recommendations for users has always been one of the hot...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/16
Inventor 欧阳元新张秦李日藩熊璋
Owner 珠海市颢腾智胜科技有限公司
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