Cross-regional cross-score collaborative filtering recommendation method and system

Abstract

The invention belongs to the field of collaborative filtering recommendation, and provides a cross-regional cross-score collaborative filtering recommendation method and system, and the method comprises the steps: dividing all users in a target domain score matrix and a source domain score matrix into active users and inactive users, and dividing all items into hot items and non-hot items; decomposing the target domain scoring matrix and the source domain scoring matrix, and extracting user implicit vectors and item implicit vectors in the target domain and the source domain; aiming at active users and hot items, respectively learning a mapping relation of user implicit vectors and item implicit vectors corresponding to a target domain and a source domain under two scoring systems; utilizing the mapping relation between the user implicit vectors and the item implicit vectors of the active users and the hot items to obtain non-active user and non-hot item characteristics on the target domain; and constructing a limited matrix decomposition model according to the inactive user and non-hot item characteristics on the target domain, predicting the score of any user on any item, and selecting the item with the highest predicted score as a recommendation result of the user.

Description

technical field [0001] The invention belongs to the technical field of collaborative filtering recommendation methods, and in particular relates to a cross-region and cross-rating collaborative filtering recommendation method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The traditional collaborative filtering recommendation algorithm is an important means to solve the problem of information overload in the era of big data. The main idea of ​​the algorithm is to learn user preferences based on user historical feedback data, provide users with personalized services, and improve user satisfaction and platform business income. , however, when the user feedback data is very sparse, the collaborative filtering algorithm often cannot effectively capture the user's preference, and the data sparsity will lead to serious overfitting o...

AI Technical Summary

Problems solved by technology

Existing models often assume that the numerical ratings of all areas in the recommendation system are relatively sparse, and adopt a consistent rating prediction strategy for different areas, ignoring the impact of rating density on the accuracy of the hidden vector of users and items, resulting in inaccurate rating predictions for sparse areas.

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