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Insights on Social Recommender Systems

Recommender Systems (RS) algorithms are growing more and more complex to follow requirements from real-world applications. Nevertheless, the slight improvement they often bring may not compensate the considerable increase in algorithmic complexity and decrease in computational performance. Contrarily, context aspects such as social awareness are still not much explored. In view of that, this paper proposes insights on how to possibly achieve more efficient and accurate predictions for recommendations by exploring multiple dimensions of a RS architecture. A framework is designed, comprised of a Facebook application called MyPopCorn and some scenarios of user neighborhood RSs are proposed. The first one investigates how to recommend movies based on a narrowed subset of collaborative data, extracted from the social connections of the active user. Secondly, connections between users enable a solution for the cold-start problem. Preferences from social connections are aggregated, producing a temporary profile of the new user. Finally, a third dimension is explored regarding evaluation metrics. Results from traditional evaluation by offline cross-validation are compared to measuring prediction accuracy of online feedback data. These insights propose how community-based RS designs might take advantage of social context features. Results show that all three proposed solutions perform better assuming some conditions. Social neighborhoods can often provide representative data for collaborative filtering user-neighborhood techniques, improving a lot the RS performance in terms of computational complexity metric without compromising prediction accuracy. Assuming a user has a dense social network, the cold-start problem can be easily tackled. Finally, rating prediction accuracy performs better when evaluated online than by offline cross-validation.