Multi-Granularity Interest Learning for Click-Through Rate Prediction

Embedding&MLP paradigm based on deep learning has been widely used in click-through rate prediction tasks. Such approaches compress user features into a fixed-length vector, which causes the bottleneck of interest learning and makes it difficult to express diverse interests of users. To improve the accuracy and diversity of recommendation, more comprehensive user interest learning is required. We present Multi-Granularity Interest Learning(MGIL)—combining the memorization of user's historical preferences with generalization of user's interests. Our fine-grained interest module learns interests at the granularity of items, preserving the memory of users' historical preferences as much as possible. The coarse-grained interest module, on the other hand, extracts multiple high-level interests from user behavior sequences to characterize multiple aspects of user interest, which are more generalized and abstract. The two modules complement each other from different granularities. We conduct extensive experiments on three real-world datasets, Movielens, Taobao and Amazon. Experimental results demonstrate that our method significantly improves the accuracy and diversity of recommendation compared to state-of-the-art models.