· 2015
Collaborative Deep Learning for Recommender Systems
- 1,658citations
- 2015year
Short summary
Collaborative Deep Learning (CDL) jointly learns deep representations for item content and performs collaborative filtering on user ratings, significantly outperforming prior methods on sparse datasets.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Collaborative Deep Learning (CDL), a model that jointly learns deep content representations and performs collaborative filtering.
- Addresses the sparsity problem common in recommender systems where user ratings are often sparse.
- Leverages both item content information and user-item interaction data for improved recommendations.
- Achieves significant performance improvements over state-of-the-art methods on three real-world datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.
The authors' abstract, as published at the source., 2015 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science