· 2019
Neural Graph Collaborative Filtering
- 3,065citations
- 2019year
Short summary
Neural Graph Collaborative Filtering (NGCF) encodes user-item interaction signals directly into embeddings, outperforming traditional methods by capturing latent collaborative effects.
AI-generated from the title and abstract; the full text is not read.
Key points
- NGCF directly encodes user-item interaction signals into embeddings.
- Traditional methods map from pre-existing user/item features, neglecting collaborative signals.
- The proposed method utilizes the user-item interaction graph to learn embeddings.
- This approach aims to better capture the collaborative filtering effect.
AI-generated from the title and abstract; the full text is not read.
Abstract
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect.
The authors' abstract, as published at the source., 2019 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science