Deep Dynamic Interest Learning With Session Local and Global Consistency for Click-Through Rate Predictions

Click-through rate (CTR) prediction is the core task in an online advertising system. How to capture the users’ dynamic interests through the behavior sequences to predict CTR is a challenging problem in the real-world applications. To address this challenge, we propose a deep dynamic interest learning by learning the local sessions and global sessions within sequences for CTR prediction. The local sessions and global sessions are used to capture the short-term dynamic interests and the long-term interests of users, respectively. To explore the heterogeneous behaviors within the global session efficiently, the interests in global sessions are forced to be consistent with that in local sessions. Then, the bi-LSTM network is adopted to capture the dynamic interests with the behaviors across global sessions effectively. Experimental results on various datasets show that the proposed method outperforms several state-of-the-art CTR prediction methods.