Multimodal Implicit Feedback for Recommender Systems
نویسنده
چکیده
In this paper, we present an overview of our work towards utilization of multimodal implicit feedback in recommender systems for small e-commerce enterprises. We focus on deeper understanding of implicit user feedback as a rich source of heterogeneous information. We present a model of implicit feedback for e-commerce, discuss important contextual features affecting its values and describe ways to utilize it in the process of user preference learning and recommendation. We also briefly report on our previous experiments within this scope and describe a publicly available dataset containing such multimodal implicit feedback.
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