Convolutional Matrix Factorization for Document Context-Aware Recommendation
Pohang University of Science and Technology · Kyung Hee University
Abstract
Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel…
Citation impact
- FWCI
- 165.64
- Percentile
- 100%
- References
- 31
Authors
5Topics & keywords
- Computer science
- Matrix decomposition
- Convolutional neural network
- Recommender system
- Context (archaeology)
- Probabilistic logic
- Artificial intelligence
- Collaborative filtering