“If I Like BLANK, What Else Will I Like?”: Analyzing a Human Recommendation Community on Reddit

While there have been several studies on how users experience algorithmic recommendations and their explanations, we know relatively little about human recommendations and which item aspects humans highlight when describing their own recommendation needs. A better understanding of human recommendation behavior could help us design better recommender systems that are more attuned to their users. In this paper, we take a step towards such understanding by analyzing a Reddit community dedicated to requesting and providing for recommendations: \(\textsf {/r/}\textsf {ifyoulikeblank}\) . After a general analysis of the community, we provide a more detailed analysis of the prevalent music requests and the example items used to ask for these recommendations. Finally, we compare these human recommendations to algorithmic recommendations to better characterize their differences. We conclude by discussing the implications of our work for recommender systems design.