A Distributional Representation Model For Collaborative Filtering
This work addresses recommendation systems for users by improving collaborative filtering, though it appears incremental as it builds on existing deep learning approaches.
The authors tackled collaborative filtering by proposing a deep learning model that jointly learns distributional representations for users and items, achieving better performance compared to current state-of-the-art algorithms.
In this paper, we propose a very concise deep learning approach for collaborative filtering that jointly models distributional representation for users and items. The proposed framework obtains better performance when compared against current state-of-art algorithms and that made the distributional representation model a promising direction for further research in the collaborative filtering.