Cold-start recommendations in Collective Matrix Factorization
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Abstract (arXiv)
This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in real-time systems. While these cold-start recommendations are not as good as warm-start ones, they were found to be of better quality than non-personalized recommendations, and predictions about new users were found to be more reliable than those about new items. The formulation proposed here resulted in improved cold-start recommendations in many scenarios, at the expense of worse warm-start ones.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Sep 2018.
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Citing papers per year: 2022: 5; 2023: 2; 2024: 1.
8 papers.
- 2022Multi Task Learning For Zero Shot Performance Prediction of Multilingual Modelscited by 20
- 2022Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendationscited by 6
- 2023Private Matrix Factorization with Public Item Featurescited by 3
- 2022A Scalable Recommendation Engine for New Users and Itemscited by 1
- 2022PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systemscited by 1
- 2022Accelerated structured matrix factorizationcited by 0
- 2023MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Informationcited by 0
- 2024Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing datacited by 0
- 2024Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data29 Dec 2024
- 2023Private Matrix Factorization with Public Item Features17 Sep 2023
- 2023MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information5 Mar 2023
- 2022Accelerated structured matrix factorization13 Dec 2022
- 2022PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems29 Sep 2022
- 2022A Scalable Recommendation Engine for New Users and Items6 Sep 2022
- 2022Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models12 May 2022
- 2022Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations1 Apr 2022