Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization

2025-07-25

AI Summary

To address the cold-start problem for new items in sequential recommendation—where items lack interaction history, rendering their embeddings unlearnable—this paper proposes a content-aware embedding initialization and lightweight fine-tuning method. The approach freezes pre-trained content encoders (e.g., text or audio feature extractors) and introduces only small, learnable delta offsets for task-specific adaptation, thereby preserving semantic stability while enabling recommendation-aware customization. Integrated into standard sequential recommendation frameworks, the method is trained end-to-end. Experiments on multiple e-commerce and music datasets demonstrate significant improvements over both frozen-embedding and full-fine-tuning baselines. It effectively mitigates the new-item cold-start issue without inducing representation drift, achieving a superior trade-off between recommendation accuracy and generalization.

Technology Category

• Machine Learning: Representation Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

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
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trained embedding. Content-based approaches, which leverage item metadata, are commonly used in such scenarios. One possible way is to use embeddings derived from content features such as textual descriptions as initialization for the model embeddings. However, directly using frozen content embeddings often results in suboptimal performance, as they may not fully adapt to the recommendation task. On the other hand, fine-tuning these embeddings can degrade performance for cold-start items, as item representations may drift far from their original structure after training. We propose a novel approach to address this limitation. Instead of entirely freezing the content embeddings or fine-tuning them extensively, we introduce a small trainable delta to frozen embeddings that enables the model to adapt item representations without letting them go too far from their original semantic structure. This approach demonstrates consistent improvements across multiple datasets and modalities, including e-commerce datasets with textual descriptions and a music dataset with audio-based representation.

Problem

• Addressing item cold start in sequential recommendations
• Improving content-based initialization for item embeddings
• Balancing frozen and fine-tuned embeddings for optimal performance

Innovation

• Content-based initialization for cold-start items
• Trainable delta to frozen embeddings
• Balances adaptation and semantic structure preservation