Cold-Start Recommendation Task
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A Cold-Start Recommendation Task is a machine learning task that aims to generate personalized recommendations for users or items with little to no historical interaction data.
- AKA: Cold-Start Recommender Task, New User/Item Recommendation Task, Zero-Interaction Recommendation Task.
- Context:
- Task Input: Limited or no historical user-item interaction data.
- Optional Input: User demographics, item metadata, contextual information (e.g., time, location).
- Task Output: Personalized item recommendations for new users or recommendations of new items to existing users.
- Task Performance Measures: Precision@K, Recall@K, NDCG@K, Mean Reciprocal Rank (MRR), Root Mean Square Error (RMSE).
- Task Objective: Provide accurate and relevant recommendations despite the absence of sufficient historical interaction data.
- It can be systematically solved and automated by a Cold-Start Recommendation System.
- It can involve strategies such as content-based filtering, leveraging item attributes to recommend to new users.
- It can utilize collaborative filtering by identifying similarities between users or items based on available data.
- It can incorporate hybrid approaches combining content-based and collaborative filtering methods.
- It can employ meta-learning techniques to quickly adapt models to new users or items with minimal data.
- It can benefit from transfer learning by applying knowledge from related domains to the cold-start scenario.
- It can address challenges in various domains, including e-commerce, streaming services, and social media platforms.
- ...
- Task Input: Limited or no historical user-item interaction data.
- Example(s):
- Implementing a content-based filtering system to recommend products to first-time users on an e-commerce platform.
- Using meta-learning approaches like MeLU to personalize recommendations for new users with minimal interaction history.
- Applying hybrid recommendation models to suggest newly added movies to users on a streaming service.
- ...
- Counter-Example(s):
- Warm-Start Recommendation Task, which deals with users or items that have ample historical interaction data.
- General Recommendation Task, focusing on overall recommendation strategies without the specific constraints of cold-start scenarios.
- Popularity-Based Recommendation Task, which recommends items based on overall popularity rather than personalized preferences.
- Cold-Start Classification Task, which focuses on assigning labels to new or rare classes with minimal or no labeled data, rather than recommending items based on user-item interactions.
- ...
- See: Cold-Start Recommendation System, Content-Based Filtering, Collaborative Filtering, Meta-Learning, Transfer Learning.
References
2025a
- (ThingSolver, 2025) ⇒ "The Cold Start Problem". Retrieved: 2025-05-16.
- QUOTE: "The cold start problem is the challenge of making recommendations for new users or items in a recommender system when there is not enough data. ... Solutions include content-based filtering, demographic data, and hybrid recommender systems that combine multiple approaches to mitigate the lack of initial information.
2025b
- (Wikipedia, 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.
2025c
- (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.
2024
- (Milvus Blog, 2024) ⇒ Milvus Blog. (2024). "What is the cold-start problem in recommender systems?".
- QUOTE: The cold-start problem in recommender systems refers to the challenge of providing accurate recommendations when there’s insufficient data about new users, items, or interactions. ... Hybrid approaches that combine collaborative filtering with content-based or metadata-driven techniques, as well as using demographic or contextual data, can help address the issue until enough interaction data is collected.
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.