TY - GEN
T1 - Dual-regularized one-class collaborative filtering
AU - Yao, Yuan
AU - Tong, Hanghang
AU - Yan, Guo
AU - Xu, Feng
AU - Zhang, Xiang
AU - Szymanski, Boleslaw K.
AU - Lu, Jian
N1 - Publisher Copyright:
Copyright 2014 ACM.
PY - 2014/11/3
Y1 - 2014/11/3
N2 - Collaborative filtering is a fundamental building block in many rec-ommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples. In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9% - 21.1% improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.
AB - Collaborative filtering is a fundamental building block in many rec-ommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples. In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9% - 21.1% improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.
KW - Dual reg-ularization
KW - One-class collaborative filtering
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=84937605010&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937605010&partnerID=8YFLogxK
U2 - 10.1145/2661829.2662042
DO - 10.1145/2661829.2662042
M3 - Conference contribution
AN - SCOPUS:84937605010
T3 - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
SP - 759
EP - 768
BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Y2 - 3 November 2014 through 7 November 2014
ER -