Recommendation system and method based on relationship type cooperative topic regression

Abstract

The invention discloses a recommendation system and a recommendation method based on relationship type cooperative topic regression. The system at least comprises an RCTR (Relationship type Cooperative Topic Regression) model establishing module, a parameter studying module and a predicted value calculating module, wherein the RCTR model establishing module is used for integrating user-item rating information, item content information and a relation structure between items into a hierarchy bayesian model to establish an RCTR model; the parameter studying module is used for utilizing maximum posteriori estimation to study parameters in the RCTR model, and finally obtaining a parameter user implicit vector, an item implicit vector, an item relation vector and a full posteriori possibility of an item topic ratio; and the predicted value calculating module is used for utilizing the user implicit vector, the item topic ratio and point estimation of item implicit deviation to calculate a predicated value of evaluation by using a predicted value calculating formula. According to the recommendation system and the method disclosed by the invention, the user-item rating information, the item content information and the relation structure between the items is integrated to one hierarchy bayesian model seamlessly to integrate a social network between the items into a recommendation process, so that the recommendation accuracy is improved.

Description

technical field [0001] The present invention relates to a recommendation system and method, in particular to a recommendation system and method based on relational collaborative topic regression. Background technique [0002] Recommender systems play an important role in the efficient use and search of information. For example, Amazon uses the recommendation system to recommend products for its customers, and Netflix (an online video rental provider) also uses the recommendation system to recommend movies and episodes. Currently, recommender systems mainly include content-based methods and coordinated filtering (CF)-based methods. [0003] Content-based methods use user or product profile information to make recommendations, and collaborative filtering-based methods use users' past activities or preferences (such as user ratings for items) to make predictions without using any user or product profile information. Due to the issue of privacy protection, it is generally far ...

AI Technical Summary

Problems solved by technology

CTR does not take into account the relationship between items

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