Merging user and item based collaborative filtering to alleviate data sparsity

Surya Kant () and Tripti Mahara
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Surya Kant: IIT Roorkee
Tripti Mahara: IIT Roorkee

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 1, No 19, 173-179

Abstract: Abstract Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF) algorithm, is one of the most widely accepted approaches to provide service recommendations. It provides personalized and automated suggestions to customers to select variety of products. Memory based algorithms mainly have two kinds of algorithms: User-based and Item-based algorithms. The User-based CF algorithm recommends items by finding similar users. Contrary to User-based CF, an Item-based CF algorithm recommends items by finding similar items. The core of memory based CF technologies is to calculate similarity among users or items. However, due to inherent sparsity, a large number of entries (ratings) in user-item rating matrix are missing. This results in only few available ratings to make prediction for the unknown ratings. This results in poor prediction quality of the CF algorithm. In this paper a hybrid approach is presented that combines user-based CF and item-based CF. It also leverage the biclustering technique to reduce the dimensionality. The biclustering helps to cluster all users/items into several groups. These clusters are then used to measure users/items similarities based on their respective parent groups. To obtain individual prediction, it adopts the user-based and item-based CF schemes based on the computed similarity respectively. Finally it combines the resultant predictions of each model to make final predictions. Interestingly, experiments demonstrated that the proposed approach outperforms the traditional user-based, item-based and some state of the art recommendation approaches in terms of accuracy of prediction and quality of recommendations.

Keywords: Collaborative filtering; Recommendation system; Information filtering; Clustering; Bi-clustering (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-016-0500-9

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