Sparse Contrastive Learning for Content-Based Cold Item Recommendation

2026-04-14

AI Summary

This work addresses the challenge of effectively recommending cold-start items in recommender systems, which suffer from a lack of user interaction data. To overcome this limitation, the authors propose a purely content-driven modeling approach that trains a content encoder to map items into a similarity space aligned with user preferences. They introduce a sparse-sampling Softmax loss based on the α-entmax activation function, which automatically suppresses gradient updates from irrelevant negative samples. Furthermore, a knowledge distillation mechanism is incorporated into the training framework to enhance model generalization. Experimental results demonstrate that the proposed method significantly outperforms existing cold-start and standard sampled Softmax approaches in ranking accuracy and notably improves recommendation fairness for cold-start items.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Item cold-start is a pervasive challenge for collaborative filtering (CF) recommender systems. Existing methods often train cold-start models by mapping auxiliary item content, such as images or text descriptions, into the embedding space of a CF model. However, such approaches can be limited by the fundamental information gap between CF signals and content features. In this work, we propose to avoid this limitation with purely content-based modeling of cold items, i.e. without alignment with CF user or item embeddings. We instead frame cold-start prediction in terms of item-item similarity, training a content encoder to project into a latent space where similarity correlates with user preferences. We define our training objective as a sparse generalization of sampled softmax loss with the $α$-entmax family of activation functions, which allows for sharper estimation of item relevance by zeroing gradients for uninformative negatives. We then describe how this Sampled Entmax for Cold-start (SEMCo) training regime can be extended via knowledge distillation, and show that it outperforms existing cold-start methods and standard sampled softmax in ranking accuracy. We also discuss the advantages of purely content-based modeling, particularly in terms of equity of item outcomes.

Problem

• cold-start
• content-based recommendation
• item similarity
• sparse contrastive learning
• recommendation equity

Innovation

• sparse contrastive learning
• cold-start recommendation
• content-based modeling
• α-entmax
• sampled softmax