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Recommendation systems usually try to “guess” a user's preferences from the system's view. We study another side of recommendation: active opinion-formation from the perspective of the user. In real life, a user's opinion evolves with time and refines when new evidence occurs. Then, how does an online user form his/her own opinion actively in large social networks? The problem has three challenges:...
The goal of a trust-based recommendation system is to predict unknown ratings based on the ratings expressed by trusted friends. However, most of the existing work only considers the ratings at the current time slot. In real life, a user receives the influence of different opinions sequentially; accordingly, his opinion evolves over time. We propose a novel rating prediction scheme, FluidRating, which...
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