Conformative Filtering for Implicit Feedback Data
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Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent this problem by making various assumptions regarding the unconsumed items, which fail to hold when the user did not consume an item because she was unaware of it. In this paper we propose Conformative Filtering (CoF) as a novel method for addressing the lack of negative examples in implicit feedback. The motivation is that if there is a large group of users who share the same taste and none of them consumed an item, then it is highly likely that the item is irrelevant to this taste. We use Hierarchical Latent Tree Analysis (HLTA) to identify taste-based user groups, and make recommendations for a user based on her memberships in the groups. Experiments on real-world datasets from different domains show that CoF has superior performance compared to other baselines and more than 10 improvement in Recall@5 and Recall@10 is observed.
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