Abstract
Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.
Highlights
Recommendation systems are increasingly drawing the attention of the practitioners and the academics
Personal recommendation systems aim to provide a single user with relevant product or service recommendations, while group recommendation systems recommend items for a group of users [15]. e major differences between these two categories can be highlighted in terms of system design, user interaction, and business purposes
Personal recommendation systems involve explicit and implicit user-item interactions where each user interacts with items separately, whereas group recommendation systems involve interactions between groups of users and items where each user is represented as a part of a group
Summary
Kassak et al [40] combined collaborative and content-based methods and converted the recommendations for individual users into group recommendations by utilizing the conjunctive aggregation function By doing so, it can better address group conflict preferences and improve the quality of group recommendations. Top personal recommendations for the individual users are provided based on selected candidate items considering users’ long-term and shortterm preference profiles. Neural collaborative filtering proposed by He et al [46] has been improved by incorporating users’ supplementary information, including users’ gender and location, to select more precise candidate items because users with similar demographic (e.g., gender and geographic location) features tend to have similar preferences [2]. Based on the user u1 interaction, user u1 interacted with four features from two items. e feature f1 is represented in both the items that the user has Complexity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
