A Hybrid Based RecommendationSystem based on Clustering and Association

Jaimeel M. Shah, Lokesh Sahu

DOI: http://dx.doi.org/10.5138/bjdmn.v5i1.1644


Recommendation systems play an important role in filtering and customizing the desired information. Recommender system are divided into 3 categories i.e collaborative filtering , contentbased filtering, and hybrid filtering are the most adopted techniques being utilized in recommender systems. The main aim of this paper is to recommend the best suitable items to the user. In this paper the approach is to cluster the data and applying the association mining over clustering. The paper describes about different hybridization methods and discuss various limitations of current recommendation methods such as cold-start problem ,Graysheep problem,how to find the similarity between users and items and discuss possible extensions that can improve recommendation capabilities in range of applications extensions such as , improvement of understanding of users and items incorporation ofthe contextual information into the recommendation process, support for multicriteria ratings.


Recommendation system, Collaborative filtering (CBF) , Content filtering(CF), Hybrid filtering , Coldstart , Graysheep , association mining, clustering

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