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Found 3 papers, 3 papers with code
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF).
Item Graph Convolution Collaborative Filtering for Inductive Recommendations
We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted.
On the instability of embeddings for recommender systems: the case of Matrix Factorization
In this paper we question the reliability of the embeddings learned by Matrix Factorization (MF).
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