Collaborative Multi-key Learning with an Anonymization Dataset for a Recommender System

Balancing accuracy and privacy is an important tradeoff problem for information systems, including recommender systems. To achieve high performance, modern recommender systems tend to use as much information as possible. This trend is borne out by the increasing number of studies of hybrid methods that combine rating and auxiliary information. However, because of privacy concerns, in many cases, service providers can not require users to give their personal information. Therefore, numerous earlier reported methods only use item attributes for auxiliary information. To address these shortcomings, our manuscript provides a method to extract user profiles without using demographic data. Our model learns user and item latent variables through two separate deep neural networks and also learns implicit relations between users and items using the information and their ratings. Experiments verified that our model is a more effective recommender system than state-of- the-art baselines.