Cold-Start Recommendation System

A Cold-Start Recommendation System is a recommender system that implements cold-start recommendation algorithms, methods, or models to systematically and automatically solve a cold-start recommendation task.

• AKA: Cold-Start Recommender, New User/Item Recommender, Zero-Interaction Recommender System.
• Context:
  • It can utilize algorithms, methods, techniques, and models:
    • Content-based filtering, for leveraging item/user attributes when interaction data is absent.
    • Collaborative filtering, to infer preferences using patterns among similar users or items.
    • Meta-learning, for rapidly adapting models to new user/item contexts with minimal data.
    • Transfer learning, to transfer knowledge from other domains or tasks to the cold-start scenario.
    • Matrix factorization, for learning latent representations that support generalized recommendations.
    • Graph-based methods, to incorporate relational knowledge from user-item interaction networks.
  • It can recommend items to new users based on side information like demographics or profiles.
  • It can promote new items to existing users by analyzing content metadata and user similarity.
  • It can solve both user cold-start (new users) and item cold-start (new items) scenarios.
  • It can be integrated with hybrid recommendation architectures to balance content and interaction signals.
  • It can support applications in e-commerce, education platforms, streaming services, and more.
  • It can improve onboarding experiences by providing relevant suggestions with minimal effort.
  • It can scale across domains by using generalizable models trained on related datasets.
  • ...
• Example(s):
  • A movie recommendation system that suggests films to new users based on demographic profiles and viewing habits of similar users.
  • A product recommender that promotes new items on an e-commerce site using item descriptions and user preferences.
  • Meta-learning-powered recommenders that personalize content after a few user interactions (e.g., MeLU).
  • ...
• Counter-Example(s):
  • Standard Recommendation System, which relies heavily on historical user-item interaction data.
  • Warm-Start Recommender, which assumes a fully trained system with dense feedback signals.
  • Cold-Start Classification System, which classifies unseen classes, not recommend items.
  • ...
• See: Cold-Start Recommendation Benchmarking Task, Cold-Start Classification Task, Content-Based Filtering, Transfer Learning, Large Language Model.

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References

2025a

• (Wikipedia Contributors, 2024) ⇒ Wikipedia Contributors. (2024). "Cold start (recommender systems)". Retrieved:2025-05-16.
  • QUOTE: The cold start problem is a well known and well researched problem for recommender systems. ... There are three cases of cold start: new community (systemic bootstrapping), new item, and new user. ... Techniques to address cold start include using content-based characteristics, personality models, and active learning to elicit informative user ratings. Collaboration among agents and leveraging similarities between items or users can also help mitigate the problem.

2025b

• (Zhang et al., 2025) ⇒ Weizhi Zhang, et al.. (2025). "Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap". arXiv Preprint.
  • QUOTE: Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. ... We provide a comprehensive review and discussion on the roadmap, related literature, and future directions of CSR, including how existing CSR utilizes information from content features, graph relations, domain information, to the world knowledge possessed by LLMs."

2022

• (Wang et al., 2022) ⇒ Shuang Wang, Yongchao Jin, Yong Liu, Jianxun Lian, Fuzheng Zhang, Xing Xie, & Guangzhong Sun. (2022). "A survey on cold-start recommendation". In: Expert Systems with Applications.
  • QUOTE: "This survey reviews cold-start recommendation methods, categorizing them into content-based, collaborative filtering, and hybrid approaches. It highlights the importance of side information (e.g., user profile, item attributes) and transfer learning for alleviating the cold-start problem, and discusses open challenges such as data sparsity and privacy."

2021

• (Lin et al., 2021) ⇒ Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, & Bin Wang. (2021). "Task-adaptive Neural Process for User Cold-Start Recommendation". In: ACM Transactions on Information Systems.
  • QUOTE: "User cold-start recommendation aims to provide accurate recommendations for users with limited or no historical interactions. The proposed Task-adaptive Neural Process framework leverages meta-learning and task adaptation to generalize from seen users to unseen users, improving cold-start recommendation performance on real-world datasets."

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