A CLUSTER-BASED SIMILARITY FUSION APPROACH FOR SCALING-UP COLLABORATIVE FILTERING RECOMMENDER SYSTEM

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GOHARI FAEZEH SADAT; TAROKH MOHAMMAD JAFAR

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Paper Information

Journal: INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH Year:2014 | Volume:6 | Issue:2 Page(s): 41-52

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Title

A CLUSTER-BASED SIMILARITY FUSION APPROACH FOR SCALING-UP COLLABORATIVE FILTERING RECOMMENDER SYSTEM

Author(s)

GOHARI FAEZEH SADAT | TAROKH MOHAMMAD JAFAR | Issue Writer Certificate

Keywords

COMPONENT

RECOMMENDER SYSTEMS

COLLABORATIVE FILTERING

SIMILARITY FUSION

CLUSTERING

Abstract

Collaborative Filtering (CF) recommenders work by collecting user ratings for items in a given domain and computing similarities between users or items to produce recommendations. The user-item rating database is extremely sparse. This means the number of ratings obtained is very small compared with the number of ratings that need to be predicted. CF suffers from the sparsity problem, resulting in poor quality recommendations and reduced coverage. Further, a CF algorithm needs calculations that are very expensive and grow non-linearly with the number of users and items in a database. Incited by these challenges, we present Cluster-Based SIMILARITY FUSION (CBSF), a new hybrid COLLABORATIVE FILTERING algorithm which can deal with the sparsity and scalability issues simultaneously. By the use of carefully selected clusters of users and items, CBSF reduces the computational cost of traditional CF, while retaining high accuracy. Experimental results demonstrate that apart from being scalable, CBSF leads to a better precision and coverage for the recommendation engine.

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APA: Copy

GOHARI, FAEZEH SADAT, & TAROKH, MOHAMMAD JAFAR. (2014). A CLUSTER-BASED SIMILARITY FUSION APPROACH FOR SCALING-UP COLLABORATIVE FILTERING RECOMMENDER SYSTEM. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 6(2), 41-52. SID. https://sid.ir/paper/315135/en

Vancouver: Copy

GOHARI FAEZEH SADAT, TAROKH MOHAMMAD JAFAR. A CLUSTER-BASED SIMILARITY FUSION APPROACH FOR SCALING-UP COLLABORATIVE FILTERING RECOMMENDER SYSTEM. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2014;6(2):41-52. Available from: https://sid.ir/paper/315135/en

IEEE: Copy

FAEZEH SADAT GOHARI, and MOHAMMAD JAFAR TAROKH, “A CLUSTER-BASED SIMILARITY FUSION APPROACH FOR SCALING-UP COLLABORATIVE FILTERING RECOMMENDER SYSTEM,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 6, no. 2, pp. 41–52, 2014, [Online]. Available: https://sid.ir/paper/315135/en

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