%0 Conference Paper %1 Wang_2022 %A Wang, Qinqin %A Tragos, Elias %A Hurley, Neil %A Smyth, Barry %A Lawlor, Aonghus %A Dong, Ruihai %B Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization %D 2022 %I ACM %K explanation knowledge-graph recommender umap2022 %P 79-88 %R 10.1145/3503252.3531316 %T Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation %U https://doi.org/10.1145%2F3503252.3531316 %X A recommendation engine that relies solely on interactions between users and items will be limited in its ability to provide accurate, diverse and explanation-rich recommendations. Side information should be taken into account to improve performance. Methods like Factorisation Machines (FM) cast recommendation as a supervised learning problem, where each interaction is viewed as an independent instance with side information encapsulated. Previous studies in top-K recommendation have incorporated knowledge graphs (KG) into the recommender system to provide rich information about the relationships between users, items and entities. Nevertheless, these studies do not explicitly capture the preference of users for the side information. Furthermore, some studies explain the recommendation, but there is no unified method of measuring explanation quality.

@inproceedings{Wang_2022, abstract = {A recommendation engine that relies solely on interactions between users and items will be limited in its ability to provide accurate, diverse and explanation-rich recommendations. Side information should be taken into account to improve performance. Methods like Factorisation Machines (FM) cast recommendation as a supervised learning problem, where each interaction is viewed as an independent instance with side information encapsulated. Previous studies in top-K recommendation have incorporated knowledge graphs (KG) into the recommender system to provide rich information about the relationships between users, items and entities. Nevertheless, these studies do not explicitly capture the preference of users for the side information. Furthermore, some studies explain the recommendation, but there is no unified method of measuring explanation quality.}, added-at = {2022-07-25T04:10:36.000+0200}, author = {Wang, Qinqin and Tragos, Elias and Hurley, Neil and Smyth, Barry and Lawlor, Aonghus and Dong, Ruihai}, biburl = {https://www.bibsonomy.org/bibtex/21a4f353fda92d7dbb0b88c53e40fb7c4/brusilovsky}, booktitle = {Proceedings of the 30th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation | Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3503252.3531316}, interhash = {f3e006713b71fe3d2a2217d7cc2a029c}, intrahash = {1a4f353fda92d7dbb0b88c53e40fb7c4}, keywords = {explanation knowledge-graph recommender umap2022}, month = jul, pages = {79-88}, publisher = {{ACM}}, timestamp = {2022-07-25T04:10:36.000+0200}, title = {Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation}, url = {https://doi.org/10.1145%2F3503252.3531316}, year = 2022 }