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Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus
https://arxiv.org/abs/2510.10440 http…

Does Weighting Improve Matrix Factorization for Recommender Systems?
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interactions. This strategy has been shown to improve performance for certain algorithms. In this paper, we conduct a systematic study of various weighting schemes and matrix factorization algorithms. Somewhat surprisingly, we find that training with unweighted data can perform comparably …

Crosslisted article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra
Zhiyan Ding, Lin Lin, Yilun Yang, Ruizhe Zhang