# Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting

arXiv: https://arxiv.org/abs/2508.06455 · HTML: https://mlsanity.com/paper/2508.06455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs](https://mlsanity.com/paper/2409.18721/) (2024)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders](https://mlsanity.com/paper/2507.16289/) (2025)
- [RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders](https://mlsanity.com/paper/2408.02354/) (2024)
- [Unsupervised Proxy Selection for Session-based Recommender Systems](https://mlsanity.com/paper/2107.03564/) (2021)
- [Autoregressive Generation Strategies for Top-K Sequential Recommendations](https://mlsanity.com/paper/2409.17730/) (2024)
- [Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering](https://mlsanity.com/paper/1610.04850/) (2016)
- [Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks](https://mlsanity.com/paper/2205.04490/) (2022)
- [The complementarity of a diverse range of deep learning features extracted from video content for video recommendation](https://mlsanity.com/paper/2011.10834/) (2020)
- [A novel graph-based model for hybrid recommendations in cold-start scenarios](https://mlsanity.com/paper/1808.10664/) (2018)
- [Collaborative Filtering vs. Content-Based Filtering: differences and similarities](https://mlsanity.com/paper/1912.08932/) (2019)
- [The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case Study](https://mlsanity.com/paper/2207.08713/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recommendation Is a Dish Better Served Warm](https://mlsanity.com/paper/2508.07856/) (2025)

## Abstract (arXiv)

Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degrade predictive performance, whereas an excessive number of features increases computational demands, leading to higher memory consumption and prolonged training times. To address this, we propose a feature selection strategy that prioritizes the user behavioral information. Our method enhances the feature representation by incorporating correlations from collaborative behavior data using a hybrid matrix factorization technique and then ranks features using a mechanism based on the maximum volume algorithm. This approach identifies the most influential features, striking a balance between recommendation accuracy and computational efficiency. We conduct an extensive evaluation across various datasets and hybrid recommendation models, demonstrating that our method excels in cold-start scenarios by selecting minimal yet highly effective feature subsets. Even under strict feature reduction, our approach surpasses existing feature selection techniques while maintaining superior efficiency.