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, RuihaiSmyth, 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 systemsNatural language generationInformation retrievalCollaborative filteringPersonalization
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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