Doubly Label Consistent Autoencoder: Accounting User and Item Metadata in Recommender Systems

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Abstract

Recent studies have experimentally shown that autoencoder based formulations for collaborative filtering can outperform other approaches. However, prior studies in this area, were either based on only ratings information; and at most accounted for either user or item metadata but not both. The metadata was appended to the ratings and just passed on as inputs. This is the most that can be done in the standard neural network based autoencoder formulation. However, collaborative filtering is a highly under-determined problem, therefore being able to utilize maximum information will naturally boost results. Previous autoencoder based formulations either had to leave out user metadata or item metadata. This is the first work, that proposes to modify the autoencoder to account for both user and item metadata. Results using explicit metadata (e.g. age, gender, occupation for users and genre for movies) and implicit metadata (neighborhood information) on our proposed formulation improves upon the state-of -the-art techniques considerably.

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