TY - GEN
T1 - CAFE
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
AU - Xian, Yikun
AU - Fu, Zuohui
AU - Zhao, Handong
AU - Ge, Yingqiang
AU - Chen, Xu
AU - Huang, Qiaoying
AU - Geng, Shijie
AU - Qin, Zhou
AU - De Melo, Gerard
AU - Muthukrishnan, S.
AU - Zhang, Yongfeng
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods.
AB - Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods.
KW - explainable recommendation
KW - knowledge graph
KW - neural symbolic reasoning
KW - path reasoning
KW - recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85092721297&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092721297&partnerID=8YFLogxK
U2 - 10.1145/3340531.3412038
DO - 10.1145/3340531.3412038
M3 - Conference contribution
AN - SCOPUS:85092721297
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1645
EP - 1654
BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 19 October 2020 through 23 October 2020
ER -