Recommend interesting items: How can social curiosity help?

The ultimate goal of recommender systems is to suggest appealing items that users are interested in. Traditional recommender systems are built based on a general consensus that users’ preferences reflect their underlying interests. Therefore, various collaborative filtering techniques have been proposed to discover items that best match users’ preferences through estimating ratings for items accurately. However, determining the interestingness of items based on user preferences alone is not sufficient. In human psychology, researchers have found an important intrinsic motivation, i.e., curiosity, for seeking interestingness in social context. Instead of focusing on users’ preferences, curiosity highlights the impact of the unknown and unexpectedness on a person’s feeling of interestingness. In light of this, we propose a novel recommendation model which recommends items by taking consideration of the target users’ curiosity in addition to their personal preferences. To model user curiosity, we adopt a psychologically inspired approach and transpose Berlyne’s theory of curiosity into a computational process. Three key curiosity-stimulating factors, including surprise, uncertainty and conflict, are modelled to estimate user’s curiosity for each item. The proposed recommendation model is evaluated with two large-scale real world datasets and the experimental results highlight that the consideration of social curiosity significantly improves recommendation precision, coverage and diversity.