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Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate auser's preference as a latent vector by encoding their historical interactions.However, users often show diverse interests, which significantly increases thelearning difficulty. In order to capture multifaceted user preferences,existing recommender systems either increase the encoding complexity or extendthe latent representation dimension. Unfortunately, these changes inevitablylead to increased training difficulty and exacerbate scalability issues. Inthis paper, we propose a novel and efficient CF framework called AttentiveMulti-modal AutoRec (AMA) that explicitly tracks multiple facets of userpreferences. Specifically, we extend the Autoencoding-based recommender AutoRecto learn user preferences with multi-modal latent representations, where eachmode captures one facet of a user's preferences. By leveraging the attentionmechanism, each observed interaction can have different contributions to thepreference facets. Through extensive experiments on three real-world datasets,we show that AMA is competitive with state-of-the-art models under the OC-CFsetting. Also, we demonstrate how the proposed model improves interpretabilityby providing explanations using the attention mechanism.

Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering