Deep Session Interest Network for Click-Through Rate Prediction

Deep Session Interest Network for Click-Through Rate Prediction

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, Keping Yang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2301-2307. https://doi.org/10.24963/ijcai.2019/319

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies overlook the intrinsic structure of the sequences: the sequences are composed of sessions, where sessions are user behaviors separated by their occurring time. We observe that user behaviors are highly homogeneous in each session, and heterogeneous cross sessions. Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users' multiple historical sessions in their behavior sequences. We first use self-attention mechanism with bias encoding to extract users' interests in each session. Then we apply Bi-LSTM to model how users' interests evolve and interact among sessions. Finally, we employ the local activation unit to adaptively learn the influences of various session interests on the target item. Experiments are conducted on both advertising and production recommender datasets and DSIN outperforms other state-of-the-art models on both datasets.
Keywords:
Machine Learning: Classification
Machine Learning: Data Mining
Machine Learning: Deep Learning
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Information Retrieval