Social Circle-Enhanced Fashion Recommendations System

Abstract

When shopping for fashionable clothing items, consumers frequently experience indecision and struggle to make choices, resulting in a stalling of the purchasing process. In such scenarios, most often they need support of their friends from their social circle to choose suitable clothes for different events. To provide decision-making support, considerable research has focused on generating social-aware recommendations that incorporate input from the user’s social circle. However, there has been minimal research dedicated to develop and evaluate such systems that could assess the importance of social circles in producing social-aware fashion recommendations and identifying factors that might enhance these recommendations. This paper addresses these limitations by developing a Social Circle-Enhanced Fashion Recommendation (SCEFR) System that encompasses friends feedback to generate recommendations. The SCEFR system was evaluated by conducting a user study, comparing system-generated recommendations with user choices as rank correlation coefficients. The findings indicate that inputs from the social circle alone have limited potential in generating effective social-aware recommendations. However, when the user’s shopping preferences were shared with their social circle, the quality of these recommendations significantly improved, as evidenced by a qualitative analysis of user feedback. Furthermore, in comparative analysis with the state-of-the-art (SOTA) approaches of recommendation generation, the SCEFR system informed by user’s shopping preferences demonstrated superiority.