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Found 6 papers, 3 papers with code
Dataset-Agnostic Recommender Systems
[This is a position paper and does not contain any empirical or theoretical results] Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tuning, and configuration.
Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related Venues
Rs4rs is a web application designed to perform semantic search on recent papers from top conferences and journals related to Recommender Systems.
RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks
By offering a unified platform for rigorous, reproducible evaluation across various recommendation scenarios, RBoard aims to accelerate progress in the field and establish a new standard for recommender systems benchmarking in both academia and industry.
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).
