Multi-behavior recommendation algorithm based on multi-view item semantic learning

Abstract

Multi-behavior recommender systems model and learn user preferences by using users' multiple behavioral interbehavior. However, the existing multi-behavior recommendation models usually ignore the independent features of each independent behavior and the personalized interbehavior relationship of users, and cannot fully model the item features. To this end, this paper proposes a multi-behavior recommendation algorithm based on multi-view item semantic learning. Graph neural network is used to mine item semantics from the perspective of independent behavior, and multi-head attention mechanism is used to learn the correlation between different behavior under the graph learning paradigm. At the same time, iterative propagation captures the collaborative signals in the user's multi-behavior information, and learns item semantic information from multiple perspectives. Compared with existing multi-behavior recommendation algorithms on Yelp and BeiBei datasets, considering item multi-view semantic learning can effectively improve recommendation accuracy.