Journal of Management Information Systems
A simple but effective method to incorporate trusted neighbors in recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Proceedings of the 7th ACM conference on Recommender systems
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Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of recommender systems and contribute to the well-known data sparsity and cold start problems. This paper proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users' experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments.