K neighbor-based Bayesian personalized recommendation method and device

Abstract

The invention discloses a K neighbor-based Bayesian personalized recommendation method. The K neighbor-based Bayesian personalized recommendation method comprises the following steps: 1) through behavior data of a user, seeking K neighbors of the user; 2) according to observed positive feedback items of the user and observed positive feedback items of a user group consisting of k neighbor users of the user, dividing an item set; 3) determining an item level preference relation of the user; 4) maximizing the probabilities of all the users on the item set to obtain an objective function, wherein item prediction of the user is realized by adopting a matrix decomposition model; parameters in the objective function are solved by adopting a stochastic gradient descent method. The invention further discloses a K neighbor-based Bayesian personalized recommendation device. Through the K neighbor-based Bayesian personalized recommendation method and the K neighbor-based Bayesian personalized recommendation device, mutual impact between the users is taken into account, and through the impact, the item set is divided, so that the number of unobserved items is reduced, and an adverse impact caused by data imbalance and data sparseness in the recommendation process is effectively relieved.

Description

technical field [0001] The invention belongs to the field of recommendation systems. Based on the Bayesian personalized recommendation method, the mutual influence between users is considered, and the level learning and sorting method is adopted to realize the personalized recommendation. The present invention is mainly applied in recommendation scenarios based on implicit data, especially in recommendation scenarios with sparse data and unbalanced data. Personalized recommendations. Background technique [0002] User-based collaborative filtering recommendation technology: [0003] The user-based collaborative filtering recommendation technology believes that a user will like the products that users who have similar interests and hobbies like him, so the most important step of this method is to calculate the similarity between users and generate a user-user similarity model. Recommendations are made based on the ratings of similar users on unrated items. [0004] Matrix ...

AI Technical Summary

Problems solved by technology

The traditional Bayesian personalized ranking algorithm assumes that each user is independent of each other, ignoring the mutual influence between users

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