Exploring User-oriented Social Recommendation System through Granting Users Control over a Social Group
Jeonguk Hong, Gyewon Jeon, Sangwon Lee · 2023
The limitations of accuracy-focused recommendation systems in improving user experiences have become apparent since user preferences change over time. Despite efforts to solve this issue through the examination of social information (e.g., relational data pertaining to users), capturing temporal user preferences remains a challenge. This study proposes a novel interaction method for integrating temporal user preferences into a social recommendation system. Users can highlight their preferred users within their social interactions. Through this interaction, preferences-integrated social information is incorporated into recommendations. We conducted a user test to validate the value of our proposed interaction method and found that the proposed approach had a positive impact on users’ subjective responses. The implications showed practical and theoretical improvements in user-centered recommendations.