Int. J. Computational Science and Engineering, Vol. 15, Nos. 3/4, 2017 295 Copyright © 2017 Inderscience Enterprises Ltd. Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques Pratibha Yadav and Shweta Tyagi* Shyama Prasad Mukherji College, University of Delhi, Delhi-110026, India Email: pratibha89yadav@gmail.com Email: shwetakaushik2006@gmail.com *Corresponding author Abstract: Clustering is one of the successful approaches of the model-based collaborative filtering techniques that deals with the problem of sparsity and provides quality recommendations. In the proposed work, fuzzy c-means clustering technique is adopted in order to produce item-based clusters as well as user-based clusters. Subsequently, collaborative filtering technique explores the item-based and user-based clusters and generates the list of item-based and user-based predictions, respectively. Further, to enhance the quality of recommendations, a novel weighted hybrid scheme is designed which integrates the user-based and item-based predictions to capture the influence of each active user towards item-based and user-based predictions. The proposed schemes are further categorised on the basis of re-clustering and without re-clustering under different similarity measures over sparse and dense datasets. The experimental results reveal that the variants of the proposed hybrid schemes consistently generate better results in comparison with the corresponding variants of proposed user-based schemes and the traditional item-based schemes. Keywords: recommender system; collaborative filtering; fuzzy c-means clustering; FCC; sparsity. Reference to this paper should be made as follows: Yadav, P. and Tyagi, S. (2017) ‘Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques’, Int. J. Computational Science and Engineering, Vol. 15, Nos. 3/4, pp.295–310. Biographical notes: Shweta Tyagi is currently an Assistant Professor in the Shyama Prasad Mukherji College (SPMC), University of Delhi, Delhi, India. She joined SPMC in 2013. Prior to SPMC, she has been a Faculty in the Computer Science Department, IITM, Delhi, India. She received her PhD in Computer Science from Jawaharlal Nehru University, Delhi, India in 2013, MTech degree in 2007 from Jawaharlal Nehru University, Delhi, India and MSc in Mathematics from the Indian Institute of Technology, Delhi, India in 2003. Her research interest includes machine learning, artificial intelligence, soft computing and information retrieval. Pratibha Yadav is currently an Assistant Professor in the Shyama Prasad Mukherji College (SPMC), University of Delhi, New Delhi, India. She joined SPMC in 2014. She received her MCA degree from University of Delhi, New Delhi, India in 2014. She has published many articles in international journals. Her research interest includes information retrieval, machine learning, artificial intelligence, and knowledge discovery in databases (KDD). 1 Introduction In today’s era, internet’s popularity is increasing exponentially which has led to the serious problem of information overloading. With the advancement of choices, users have a need for information retrieval and filtering techniques that suggest them based on their interest. The emerged system is known as recommender system (RS), which has become popular in research area as well as in industry since its emergence in the mid 1990s (Resnick et al., 1994; Shardanand and Maes, 1995). RS has revolutionised the field of information filtering by making personalised information available for the users. These systems recommend items for users on the basis of their preferences that a user would give to an item (Adomavicius and Tuzhilin, 2005; Bobadilla et al., 2013), the items may be music (Lee et al., 2010), movies (Christakou et al., 2007; Miller et al., 2003), books (Mooney and Roy, 1999), news (Garcin et al., 2012; Claypool et al., 1999), search queries (Adomavicius et al., 2011) and many more products used in day-to-day life. RSs are used in various applications that include TV (Barragans-Martinez et al., 2010; Yu et al., 2006), mobile (Wan-Shiou et al., 2008), financial services (Felfernig and Kiener, 2005), life insurance (Abbas et al.,