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Why I like it: multi-task learning for recommendation and explanation

Authors: 
Publication Type: 
Refereed Original Article
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
Digital Object Identifer (DOI): 
10.1145/3240323.3240365
Publication Status: 
Published
Date Accepted for Publication: 
Tuesday, 14 August, 2018
Publication Date: 
27/09/2018
Journal: 
Recommender Systems
Research Group: 
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No