Cold-Start Prediction Task
(Redirected from cold start problem)
A Cold-Start Prediction Task is an entity prediction task with little entity information (especially predictor information).
- Context:
- It can (typically) range from being an Item Cold-Start Prediction Task to being a User Cold-Start Prediction Task.
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- Example(s):
- See: Active Learning Task, Item Recommendation Task, One-Shot Learning.
References
2009
- (Jamali et al., 2009) ⇒ Mohsen Jamali, and Martin Ester. (2009). “TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557067
- QUOTE: Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items.
2002
- (Schein et al., 2002) ⇒ Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock. (2002). “Methods and Metrics for Cold-Start Recommendations.” In: Proceedings of the 25th ACM SIGIR Conference (SIGIR 2002) doi:10.1145/564376.564421.
- QUOTE: One difficult, though common, problem for a recommender system is the cold-start problem, where recommendations are required for items that no one (in our data set) has yet rated.1 Pure collaborative filtering cannot help in a cold-start setting, since no user preference information is available to form any basis for recommendations.