Improving recommendation by deep latent factor-based explanation
|Title:||Improving recommendation by deep latent factor-based explanation||Authors:||Ouyang, Sixun; Lawlor, 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 Systems; Latent 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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