Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


Recommender engines have become extremely important in recent years because the count of people using Internet for diverse purposes is growing at an overwhelming speed. Different websites work on recommender systems using different techniques like content-based filtering, collaborative filtering, or hybrid filtering. Recommender engines face various challenges like scalability problem, cold start problem and sparsity issues. Cold start problem arises when there is no sufficient information for the user who has recently logon into the system and no proper recommendations can be made. This paper proposes a novel approach which applies fuzzy c-means clustering technique to address user cold start problem. Also, a comparison is made between fuzzy c-means clustering and the traditional k-means clustering method based on different set of users and thus it has been proved that the accuracy of fuzzy c-means approach is better than k-means for larger size of dataset.


Collaborative filtering Cold start Recommender system Fuzzy clustering 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia

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