%0 Conference Paper %1 Ma:2019:JLE:3308558.3313607 %A Ma, Weizhi %A Zhang, Min %A Cao, Yue %A Jin, Woojeong %A Wang, Chenyang %A Liu, Yiqun %A Ma, Shaoping %A Ren, Xiang %B The World Wide Web Conference %C New York, NY, USA %D 2019 %I ACM %K explanation knowledge-graph recommender %P 1210--1221 %R 10.1145/3308558.3313607 %T Jointly Learning Explainable Rules for Recommendation with Knowledge Graph %U http://doi.acm.org/10.1145/3308558.3313607 %X Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate induction of explainable rules from knowledge graph with construction of a rule-guided neural recommendation model. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments1 show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over “noisy” item knowledge graphs, generated by linking item names to related entities. %@ 978-1-4503-6674-8

@inproceedings{Ma:2019:JLE:3308558.3313607, abstract = {Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate induction of explainable rules from knowledge graph with construction of a rule-guided neural recommendation model. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments1 show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over “noisy” item knowledge graphs, generated by linking item names to related entities.}, acmid = {3313607}, added-at = {2019-12-25T19:19:06.000+0100}, address = {New York, NY, USA}, author = {Ma, Weizhi and Zhang, Min and Cao, Yue and Jin, Woojeong and Wang, Chenyang and Liu, Yiqun and Ma, Shaoping and Ren, Xiang}, biburl = {https://www.bibsonomy.org/bibtex/29aef4b7144d0c7a207ea5f9a4d821d52/brusilovsky}, booktitle = {The World Wide Web Conference}, description = {Jointly Learning Explainable Rules for Recommendation with Knowledge Graph}, doi = {10.1145/3308558.3313607}, interhash = {8ccad91b4a2a5ecc818d3480b2b97bd5}, intrahash = {9aef4b7144d0c7a207ea5f9a4d821d52}, isbn = {978-1-4503-6674-8}, keywords = {explanation knowledge-graph recommender}, location = {San Francisco, CA, USA}, numpages = {12}, pages = {1210--1221}, publisher = {ACM}, series = {WWW '19}, timestamp = {2019-12-25T19:19:06.000+0100}, title = {Jointly Learning Explainable Rules for Recommendation with Knowledge Graph}, url = {http://doi.acm.org/10.1145/3308558.3313607}, year = 2019 }