Group recommendation method based on bidirectional tensor decomposition model

Abstract

The invention discloses a group recommendation method based on a bidirectional tensor decomposition model. The method comprises the following steps: 1) defining an interactive relationship DS representing a group G, a user U and a product I; 2) establishing a tensor decomposition model; 3) performing transform solving on the tensor decomposition model by utilizing a Bayesian personalized ranking method so as to obtain each parameter value in the tensor decomposition model; 4) obtaining group preference rg,i of the gth group on the ith product, and traversing all commodities to obtain the group preference of the gth group on all the products; and 5) performing descending sort on the group preference of the gth group on all the products, and selecting the previous N products to serve as a recommended product list to be pushed to the gth group. According to the method disclosed by the invention, the personal preference is modeled into a bidirectional process, the real formation process of the personal preference can be effectively reflected, the group recommendation precision is improved, and the method has better robustness.

Description

technical field [0001] The invention belongs to the field of group recommendation, in particular to a group recommendation (BTF-GR) method based on a bidirectional tensor decomposition model Background technique [0002] Social networking has long been an important part of the social media environment. Users will spontaneously form groups on social networking platforms, and capturing the preferences of each group will help us conduct in-depth behavioral analysis of user groups, and then recommend target products and Serve. But group recommendation is different from individual recommendation, because groups are usually composed of users with diverse preferences, so group recommendation is not easy to achieve. The core task of group recommendation is to aggregate individual preferences to generate a group recommendation result, but the existing aggregation strategies all model the formation of group preference as a one-way process, although group preference is the result of i...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of existing group recommendation strategies, the present invention proposes a group recommendation method based on a two-way tensor decomposition model, in order to reflect the interaction between the group and the individual in the individual preference modeling, and to describe the preference of the group to the individual The difference in influence, through the aggregation of accurate individual preferences to form accurate group preferences, thereby improving the accuracy of group recommendation, and suitable for accurate and stable group recommendation in large-scale and sparse data environments

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