Improving recommendation by deep latent factor-based explanation

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Title: Improving recommendation by deep latent factor-based explanation
Authors: Ouyang, SixunLawlor, Aonghus
Permanent link: http://hdl.handle.net/10197/11682
Date: 12-Feb-2020
Online since: 2020-11-10T16:54:03Z
Abstract: The latent factor methods and explanation algorithms constitute the foundation of many advanced explainable recommender systems. However, interpreting the high-dimensional latent factors has not been sufficiently addressed and continuously becomes a challenging work. Besides, only a few works have researched the use of explanation to improve recommendations. In this paper, we propose a deep learning method that generates high-quality latent factor-based explanations and efficiently ameliorating recommendations. We conduct top- K items ranking experiment on two real-world datasets and show that our method outperforms nine currently state-of-theart recommender systems in five ranking metrics. Moreover, we conduct a qualitative and quantitative analysis of users’ latent factors and reveal that we continually offer the best latent representations.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Conference Publication
Keywords: Recommender SystemsLatent representation
Language: en
Status of Item: Peer reviewed
Conference Details: The Thirty-Fourth AAAI Conference on Artificial Intelligence: Interactive and Conversational Recommendation Systems (WICRS) Workshop, New York, United States of America, 7-12 February 2020
Appears in Collections:Insight Research Collection

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