Improving recommendation by deep latent factor-based explanation
|dc.description||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||en_US|
|dc.description.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.||en_US|
|dc.description.sponsorship||Science Foundation Ireland||en_US|
|dc.title||Improving recommendation by deep latent factor-based explanation||en_US|
|dc.description.othersponsorship||Insight Research Centre||en_US|
|Appears in Collections:||Insight Research Collection|
If you are a publisher or author and have copyright concerns for any item, please email email@example.com and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.