Matrix decomposition recommendation method based on Bayesian probability with social relations and project content

Abstract

The invention discloses a matrix decomposition recommendation method based on Bayesian probability with social relations and project content. The method includes the steps that a PMF method is used for performing hidden matrix analysis on an observation evaluation matrix to obtain a hidden user characteristic matrix and a hidden project characteristic matrix; a BPMFSR method or a BPMFSRIC method is used for performing Gibbs sampling on the hidden user characteristic matrix and the hidden project characteristic matrix to obtain the hidden user characteristic matrix after sampling and the hidden project characteristic matrix after sampling; according to the hidden user characteristic matrix after sampling and the hidden project characteristic matrix after sampling, a forecast evaluation matrix is calculated, and recommendation is performed based on the forecast evaluation matrix. The method is efficient in calculation, can be applied to a recommendation system which has a large-scale data set and is based on trust or content, has a larger convergence rate, obtains more accurate recommendation results compared with other matrix decomposition methods and solves the problems of data sparseness and cold start better than other methods.

Description

technical field [0001] The invention belongs to the technical field of recommendation systems, and more specifically relates to a Bayesian probability matrix decomposition recommendation method based on social relations and item content. Background technique [0002] Recommender systems have become an important research area in the past decade. A typical recommender system attempts to predict a user's interest by collecting information about the user's evaluation of other users or items. Recommendation methods are usually divided into collaborative filtering methods and content-based methods. Collaborative filtering attempts to predict an item's rating for a particular user based on other similar users' ratings for the item. The basic idea of ​​collaborative filtering is to assume that similar users have similar tastes. Collaborative filtering methods are widely used in large commercial systems, such as Amazon (Amazon) and Netflix (Netflix). [0003] Matrix factorization...

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Problems solved by technology

[0007] In view of the above defects and improvement needs of the prior art, the present invention provides a Bayesian probability matrix decomposition method that integrates social relations and project content, the purpose of which is to alleviate the data sparsity and cold start problems often faced in recommendation systems, Get better recommendation results, thus solve the problem of data sparseness or cold start that cannot be solved well by the recommendation system, and make recommendations accurately and quickly

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