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Found 4 papers, 3 papers with code
Matryoshka Representation Learning for Recommendation
In this paper, we introduce a novel matryoshka representation learning method for recommendation (MRL4Rec), by which we restructure user and item vectors into matryoshka representations with incrementally dimensional and overlapping vector spaces to explicitly represent user preferences and item features at different hierarchical levels.
A Survey on Data-Centric Recommender Systems
Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications.
Adaptive Hardness Negative Sampling for Collaborative Filtering
Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance.
Augmented Negative Sampling for Collaborative Filtering
To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy.
