Can I read Improving Explainability of Recommendation System by Multi-sided Tensor Factorization on EtoBox?

Improving Explainability of Recommendation System by Multi-sided Tensor Factorization by Hong, Minsung; Akerkar, Rajendra; Jung, Jason J. is a Computer Science article available to read on EtoBox.

What is Improving Explainability of Recommendation System by Multi-sided Tensor Factorization about?

Recently, explainable recommender systems to improve their persuasiveness have attracted attentions. In this regard, some approaches extract information from posts or comments on items and apply them to simple and effective template. These information (e.g., topics and interests), however, are indirectly reflected to the existing recommendation algorithms or models therefore do not directly improve the recommendation accuracy. Moreover, extra resources in deriving information are required. Thereby, we propose a collaborative filtering approach using a tensor which is modeled considering 5Ws aspects and generate explanations by combining factorization results with templates. Quality and explanation of recommendations were evaluated on quantitative/qualitative analyses.

Who reads Improving Explainability of Recommendation System by Multi-sided Tensor Factorization?

Author

Hong, Minsung; Akerkar, Rajendra; Jung, Jason J.

Publisher

Taylor and Francis Group; Informa UK (Taylor & Francis); Taylor & Francis; Informa UK Limited (ISSN 0196-9722)

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)