Lu, Yichao;Yichao;LuDong, RuihaiRuihaiDongSmyth, BarryBarrySmyth2019-07-112019-07-112018 Assoc2018-10-07Recommender Systemshttp://hdl.handle.net/10197/10892RecSys '18: 12th ACM Conference on Recommender Systems, Vancouver, BC, 2–7 October 2018We 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.en© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in RecSys '18 Proceedings of the 12th ACM Conference on Recommender Systems (2018) http://doi.acm.org/10.1145/3240323.3240365Recommender systemsNatural language generationInformation retrievalCollaborative filteringPersonalizationJournal Article41210.1145/3240323.32403652018-10-26SFI/12/RC/2289https://creativecommons.org/licenses/by-nc-nd/3.0/ie/