Journal
IEEE ACCESS
Volume 10, Issue -, Pages 6218-6245Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3140121
Keywords
Recommender systems; Collaborative filtering; Urban areas; Predictive models; Licenses; Sparse matrices; Motion pictures; Collaborative filtering; group recommender systems; recommender systems
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Funding
- Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam
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To meet the demand for group activities, single-user recommender systems need to be scaled up. This paper introduces the concept of deep profiles and proposes group recommendation methods based on deep profile aggregation. Experiments have shown that group recommendations based on deep profiles are more efficient.
To meet the increasing demand for group activities, single-user recommender systems need to be scaled up to provide recommendations to groups of users. This issue is solved by aggregating item preference profiles of individual group members into a single item preference profile, thereby allowing recommendations to be created for this item preference profile. In this paper, we introduce the concept of deep profiles of users, and we propose group recommendation methods based on the aggregation of group members' deep profiles, instead of item preference profiles as in previous studies. The term deep profile refers to the users' profiles that lie deep within the recommendation algorithms. Experiments have shown that group recommendations based on deep profiles give higher efficiency in terms of F1-score and nDCG than those based on item preference profiles.
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