The Cold-start Problem: Minimal Users' Activity Estimation

TL;DR

This paper investigates the minimal user ratings needed to effectively address the cold-start problem in recommender systems by using clustering techniques to determine when enough data has been collected for accurate recommendations.

Contribution

It introduces a clustering-based method to identify the minimal ratings required for reliable user clustering, thereby reducing the cold-start problem in recommendation systems.

Findings

Clustering can effectively identify minimal ratings needed for accurate user grouping.

The approach reduces cold-start issues in movie and joke recommendation domains.

Experimental results validate the method's applicability across different domains.

Abstract

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.