Improving Explainability of Recommendation System by Multi-sided Tensor Factorization

Taylor & Francis
Cybernetics and Systems

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.