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

摘要：

面对数量庞大的用户和物品数量，推荐系统通常面临着数据稀疏的问题，为缓解此问题，提出了一个融合注意力机制和自编码器的协同过滤模型。该模型将评分信息送入一个基于自编码器的协同过滤子模型中以挖掘用户整体偏好，同时将评分信息送入一个融合了注意力机制的基于物品的协同过滤子模型中以挖掘物品与物品之间的局部依赖信息，随后将两个子模型的结果相融合，拟合出最终的结果。模型在MovieLens和Pinterest数据集上进行了实验验证，实验结果与基准相比有所改善。

关键词: 协同过滤, 用户偏好, 物品依赖, 注意力机制