Infusing Collaborative Recommenders with Distributed Representations
This work addresses the challenge of enhancing recommendation accuracy for users by integrating diverse data sources, though it appears incremental as it builds on existing neural network methods.
The paper tackled the problem of improving recommender systems by leveraging multiple data sources (ratings, tagging, and item data) to learn distributed representations using neural networks, resulting in better rating prediction performance than traditional representations and further gains when combined.
Recommender systems assist users in navigating complex information spaces and focus their attention on the content most relevant to their needs. Often these systems rely on user activity or descriptions of the content. Social annotation systems, in which users collaboratively assign tags to items, provide another means to capture information about users and items. Each of these data sources provides unique benefits, capturing different relationships. In this paper, we propose leveraging multiple sources of data: ratings data as users report their affinity toward an item, tagging data as users assign annotations to items, and item data collected from an online database. Taken together, these datasets provide the opportunity to learn rich distributed representations by exploiting recent advances in neural network architectures. We first produce representations that subjectively capture interesting relationships among the data. We then empirically evaluate the utility of the representations to predict a user's rating on an item and show that it outperforms more traditional representations. Finally, we demonstrate that traditional representations can be combined with representations trained through a neural network to achieve even better results.