{"type":"ScholarlyArticle","title":"DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation","authors":[{"name":"Huan Yan","slug":"huan-yan","given":"Huan","family":"Yan","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,; Department of Electronic Engineering , Tsinghua University , Beijing , 100084 , China"},{"name":"Xiangning Chen","slug":"xiangning-chen","given":"Xiangning","family":"Chen","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,; Department of Electronic Engineering , Tsinghua University , Beijing , 100084 , China"},{"name":"Chen Gao","slug":"chen-gao","given":"Chen","family":"Gao","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,; Department of Electronic Engineering , Tsinghua University , Beijing , 100084 , China"},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,; Department of Electronic Engineering , Tsinghua University , Beijing , 100084 , China"},{"name":"Depeng Jin","slug":"depeng-jin","given":"Depeng","family":"Jin","affiliation":"Beijing National Research Center for Information Science and Technology (BNRist) ,; Department of Electronic Engineering , Tsinghua University , Beijing , 100084 , China"}],"abstract":"Existing web video systems recommend videos according to users' viewing history from its own website. However, since many users watch videos in multiple websites, this approach fails to capture these users' interests across sites. In this paper, we investigate the user viewing behavior in multiple sites based on a large scale real dataset. We find that user interests are comprised of cross-site consistent part and site-specific part with different degrees of the importance. Existing linear matrix factorization recommendation model has limitation in modeling such complicated interactions. Thus, we propose a model of Deep Attentive Probabilistic Factorization (DeepAPF) to exploit deep learning method to approximate such complex user-video interaction. DeepAPF captures both cross-site common interests and site-specific interests with non-uniform importance weights learned by the attentional network. Extensive experiments show that our proposed model outperforms by 17.62%, 7.9% and 8.1% with the comparison of three state-of-the-art baselines. Our study provides insight to integrate user viewing records from multiple sites via the trusted third party, which gains mutual benefits in video recommendation.","doi":"10.24963/ijcai.2019/202","url":"https://ijcai.org/proceedings/2019/202","pdf":"https://ijcai.org/proceedings/2019/0202.pdf","markdown":"https://ijcai.org/proceedings/2019/202.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/202","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":"Humans and AI","track":"Main track","number":202,"pages":{"first":1459,"last":1465},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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