LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
arXiv:2606.01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.g., statistical value) to high-dimensional embeddings (e.g., user-id embeddings, MLP representations) -- to achieve high-precision predictions. Given the immense computational costs associated with training, efficient feature selection is critical. However, existing methods encounter three primary bottlenecks: (1) they typically assume uniform feature dimensions or require costly mapping to a fixed size; (2) they struggle w
The proliferation of increasingly complex recommender systems in AI-driven applications necessitates more efficient feature selection methods to manage computational costs and improve performance, which is a growing bottleneck.
This development is crucial for any organization deploying large-scale AI recommenders, as it promises substantial reductions in computational overhead and improvements in prediction accuracy, directly impacting energy consumption and operational efficiency.
Current limitations in handling heterogeneous and sparse features for recommendation systems may be overcome, leading to more scalable and precise AI-driven personalization engines across various industries.
- · Large AI/tech companies
- · E-commerce platforms
- · Content streaming services
- · AI infrastructure providers
- · Companies with inefficient AI infrastructure
- · Less performant feature selection methods
More efficient and accurate recommender systems will emerge in production environments.
Reduced computational costs could enable the deployment of more complex AI models or a greater number of models at scale.
This efficiency could indirectly contribute to the broader energy efficiency of AI, mitigating some 'energy-bottleneck' concerns for compute.
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Read at arXiv cs.LG