This repo includes the implementation of serveral explainable recommendation algorithms developed by our team, including MTER [1], FacT [2], SAER [3], and GREENer [4]. The detailed instructions on their usage can be found in their corresponding directories. We are adding more explainable recommendation models to this repo, including our recent publication of CompExp [5], and also some baselines used in our papers, such as NRT [6], NARRE [7], etc. Stay tuned.
[1]: Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). Association for Computing Machinery, New York, NY, USA, 165–174. DOI:https://doi.org/10.1145/3209978.3210010
[2]: Yiyi Tao, Yiling Jia, Nan Wang, and Hongning Wang. 2019. The FacT: Taming Latent Factor Models for Explainability with Factorization Trees. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 295–304. DOI:https://doi.org/10.1145/3331184.3331244
[3]: Aobo Yang, Nan Wang, Hongbo Deng, and Hongning Wang. 2021. Explanation as a Defense of Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM '21). Association for Computing Machinery, New York, NY, USA, 1029–1037. DOI:https://doi.org/10.1145/3437963.3441726
[4]: Peng Wang, Renqin Cai, and Hongning Wang. 2022. Graph-based Extractive Explainer for Recommendations. In Proceedings of the ACM Web Conference 2022 (WWW ’22), April 25–29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA, 9 pages. DOI:https://doi.org/10.1145/3485447.3512168
[5]: Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng, Hongning Wang. 2022. Comparative Explanations of Recommendations. In Proceedings of the ACM Web Conference 2022 (WWW ’22), April 25–29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA, 11 pages. DOI:https://doi.org/10.1145/3485447.3512031
[6]: Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural Rating Regression with Abstractive Tips Generation for Recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). Association for Computing Machinery, New York, NY, USA, 345–354. DOI:https://doi.org/10.1145/3077136.3080822
[7]: Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-level Explanations. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1583–1592. DOI:https://doi.org/10.1145/3178876.3186070