TY - GEN
T1 - RippleNet
T2 - 27th ACM International Conference on Information and Knowledge Management, CIKM 2018
AU - Wang, Hongwei
AU - Zhang, Fuzheng
AU - Wang, Jialin
AU - Zhao, Miao
AU - Li, Wenjie
AU - Xie, Xing
AU - Guo, Minyi
PY - 2018/10/17
Y1 - 2018/10/17
N2 - To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.
AB - To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.
KW - Knowledge graph
KW - Preference propagation
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85058015610&partnerID=8YFLogxK
U2 - 10.1145/3269206.3271739
DO - 10.1145/3269206.3271739
M3 - Conference article published in proceeding or book
AN - SCOPUS:85058015610
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 417
EP - 426
BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery
Y2 - 22 October 2018 through 26 October 2018
ER -