Why I like it: Multi-task Learning for Recommendation and Explanation
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|Title:||Why I like it: Multi-task Learning for Recommendation and Explanation||Authors:||Lu, Yichao;; Dong, Ruihai; Smyth, Barry||Permanent link:||http://hdl.handle.net/10197/10892||Date:||7-Oct-2018||Online since:||2019-07-11T11:51:25Z||Abstract:||We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for explanation generation. The result is evaluated using real-world datasets to demonstrate improved rating prediction performance, compared to state-of-the-art alternatives, while producing effective, personalized explanations.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||ACM||Journal:||Recommender Systems||Start page:||4||End page:||12||Copyright (published version):||2018 Association for Computing Machinery||Keywords:||Recommender systems; Natural language generation; Information retrieval; Collaborative filtering; Personalization||DOI:||10.1145/3240323.3240365||Other versions:||https://recsys.acm.org/recsys18/||Language:||en||Status of Item:||Peer reviewed||Is part of:||RecSys '18 Proceedings of the 12th ACM Conference on Recommender Systems||Conference Details:||RecSys '18: 12th ACM Conference on Recommender Systems, Vancouver, BC, 2–7 October 2018|
|Appears in Collections:||Insight Research Collection|
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