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Social recommender systems on entertainment platforms help users discover content through compatible social relationships. As transparency increases, users can better identify well-suited friends and make informed decisions about what content to watch. This study aims to investigate the effects of social profiles and explanation levels on enhancing user experience in a recommender system. We conducted a 3 (social profile types) × 3 (explanation levels) between-subject experiment, measuring decision-making efficiency, effectiveness, information richness, transparency, recommendation quality, user satisfaction, persuasiveness, and social presence. Results showed that profiles presenting both positive and negative preferences significantly improved subjective evaluations over profiles with only negative preferences. Moreover, our findings indicated that explanation levels should be tailored to social profiles to reduce decision-making effort and enhance user experience, including recommendation quality and satisfaction. These findings offer theoretical and practical contributions for designing user-centered social recommender systems that effectively support social matching and content discovery.
Hong et al. (Wed,) studied this question.