Work

A Multi-Criteria Collaborative Filtering Approach Using Deep Learning and Dempster-Shafer Theory for Hotel Recommendations

Year: 2022

Type: article

Abstract: This paper addresses the problem of multi-criteria recommendation in the hotel industry. The main focus is to analyze user preferences from different aspects based on multi-criteria ratings and develop a new multi-criteria collaborative filtering method for hotel recommendations. Particularly, the proposed recommendation system integrates matrix factorization into a deep learning model to predict the multi-criteria ratings, and then the evidential reasoning approach is adopted to model the uncertainty of those ratings represented as mass functions in Dempster-Shafer theory of evidence. Finally, Dempster’s rule of combination is utilized to aggregate those multi-criteria ratings to obtain the overall rating for recommendation. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness and efficiency of the proposed method compared with other multi-criteria collaborative filtering methods.

Source: IEEE Access

DOI: 10.1109/access.2022.3165310

Authors: Quang-Hung Le, Toan Nguyen Mau, Roengchai Tansuchat, Van-Nam Huynh

Institutions: Quy Nhon University, Japan Advanced Institute of Science and Technology, Chiang Mai University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,850

Funders: Chiang Mai University, Bộ Giáo dục và Ðào tạo, Faculty of Economics, Chiang Mai University