关键词: 协同过滤, 用户偏好, 物品依赖, 注意力机制

Abstract:

Facing the huge number of users and items, recommendation systems usually face the problem of sparse data. To alleviate this problem, a collaborative filtering model that combines attention mechanism and autoencoder is proposed. The model sends the rating information to an autoencoder-based collaborative filtering sub-model that is used to mine the user’s overall preferences. At the same time, the rating information is fed into an item-based collaborative filtering sub-model that incorporates the attention mechanism to mine the local dependency information between items. The results in the sub-models are fused to fit the final results. The model is experimentally verified on the MovieLens and Pinterest datasets, and the experimental results are improved compared to the benchmark.

Key words: collaborative filtering, user preference, item dependence, attention mechanism