{"type":"ScholarlyArticle","title":"Deep Session Interest Network for Click-Through Rate Prediction","authors":[{"name":"Yufei Feng","slug":"yufei-feng","given":"Yufei","family":"Feng","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Fuyu Lv","slug":"fuyu-lv","given":"Fuyu","family":"Lv","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Weichen Shen","slug":"weichen-shen","given":"Weichen","family":"Shen","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Menghan Wang","slug":"menghan-wang","given":"Menghan","family":"Wang","affiliation":"Alibaba Group , Hangzhou , China; Zhejiang University , Hangzhou , China"},{"name":"Fei Sun","slug":"fei-sun","given":"Fei","family":"Sun","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Yu Zhu","slug":"yu-zhu","given":"Yu","family":"Zhu","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Keping Yang","slug":"keping-yang","given":"Keping","family":"Yang","affiliation":"Alibaba Group , Hangzhou , China"}],"abstract":"Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems.\r\nHow 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.","doi":"10.24963/ijcai.2019/319","url":"https://ijcai.org/proceedings/2019/319","pdf":"https://ijcai.org/proceedings/2019/0319.pdf","markdown":"https://ijcai.org/proceedings/2019/319.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/319","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":319,"pages":{"first":2301,"last":2307},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Deep Learning","Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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