Hybrid User-Item Based Collaborative Filtering | Zendy

Nitin Kumar | Zendy; Zhenzhen Fan | Zendy

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Hybrid User-Item Based Collaborative Filtering

Author(s) -

Nitin Kumar,

Zhenzhen Fan

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.08.222

Subject(s) - computer science , collaborative filtering , recommender system , cluster analysis , scalability , scope (computer science) , data mining , set (abstract data type) , face (sociological concept) , data set , machine learning , product (mathematics) , artificial intelligence , information retrieval , database , social science , geometry , mathematics , sociology , programming language

Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm

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