{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:38:24Z","timestamp":1783953504310,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory\n\naccuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate\n\nthe explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https:\/\/github.com\/pd90506\/AMCF.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/373","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"2690-2696","source":"Crossref","is-referenced-by-count":27,"title":["Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability"],"prefix":"10.24963","author":[{"given":"Deng","family":"Pan","sequence":"first","affiliation":[{"name":"Wayne State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangrui","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:14:45Z","timestamp":1594260885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/373"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/373","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}