The Cold-start Problem: Minimal Users' Activity Estimation
This addresses the cold-start problem for recommender systems in domains with sparse user feedback, but it is incremental as it builds on existing clustering techniques.
The paper tackles the cold-start problem in recommender systems by determining the minimal number of item ratings a new user needs to provide to be accurately assigned to a cluster, enabling recommendations; experiments on MovieLens and Jester datasets show this approach reduces the cold-start issue.
Cold-start problem, which arises upon the new users arrival, is one of the fundamental problems in today's recommender approaches. Moreover, in some domains as TV or multime-dia-items take long time to experience by users, thus users usually do not provide rich preference information. In this paper we analyze the minimal amount of ratings needs to be done by a user over a set of items, in order to solve or reduce the cold-start problem. In our analysis we applied clustering data mining technique in order to identify minimal amount of item's ratings required from recommender system's users, in order to be assigned to a correct cluster. In this context, cluster quality is being monitored and in case of reaching certain cluster quality threshold, the rec-ommender system could start to generate recommendations for given user, as in this point cold-start problem is considered as resolved. Our proposed approach is applicable to any domain in which user preferences are received based on explicit items rating. Our experiments are performed within the movie and jokes recommendation domain using the MovieLens and Jester dataset.