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Most of studies on the existing recommender system for Netflix-style sites (scenarios with explicit user feedback) focus on rating prediction, but few have systematically analyzed users’ motivations to make decisions on which items to rate. In this paper, the authors study the difficult and challenging task Item Adoption Prediction (IAP) for predicting the items users will rate or interact with. It is not only an important supplement to previous works, but also a more realistic requirement of recommendation in this scenario. To recommend the items with high Adoption Tendency, the authors develop a unified model UATM based on the findings of Marketing and Consumer Behavior. The novelty of the model in this paper includes: First, the authors propose a more creative and effective optimization method to tackle One-Class Problem where only the positive feedback is available; second, the authors systematically and conveniently integrate the user adoption information (both explicit and implicit feedbacks included) and the social contextual information with quantitatively characterizing different users’ personal sensitivity to various social contextual influences

User Adoption Tendency Modeling for Social Contextual Recommendation