Exploring social network effects on popularity biases in recommender systems
Rocío Cañamares, Pablo Castells · Conference on Recommender Systems · 2014
items ranked by popularity has been found to be a fairly competitive approach in the top-N recommendation task. In this paper we explore whether popularity should always be ex- pected to be an effective approach for recommendation, and what causes, factors and conditions determine and explain such effec- tiveness. We focus on two fundamental potential sources of biases in rating data which determine the answer to these questions: item discovery by users, and rating decision. We research the role of social communication as a major source of item discovery biases (and therefore rating biases). We undertake the study by defining a probabilistic model of such factors, and running simulations where we analyze the relationships between the effectiveness of populari- ty and different configurations of social behavior. Keywordssocial networks, evaluation, viral propagation.