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The Effect of Neighborhood Selection on Collaborative Filtering and a Novel Hybrid Algorithm



Recommender systems are widely used in industry and are still active research areas in academia. For many businesses, they have become indispensable business tools. Producing accurate results for such systems is important for the operations of the businesses. For this reason, various algorithms and approaches have been developed for recommender systems to increase the prediction accuracy. Collaborative filtering is one of the most successful approaches. In collaborative filtering, in order to predict more accurately, it is recommended to determine user2019s active neighbors. k-nearest neighbor (k-NN) algorithm is one of the most widely used neighbor selection algorithms. However, k-NN algorithm uses a fixed k value that reduces the accuracy of the prediction. In this paper, we present two novel approaches to increase the prediction accuracy of recommender systems; k%-nearest neighbor (k%-NN) algorithm to determine the appropriate k value for a user and a hybrid algorithm that combines a collaborative filtering technique and content-based approach. Our test results demonstrate that k%-NN algorithm increases the average prediction accuracy compared to the traditional k-NN algorithm. Additionally, when the proposed hybrid algorithm is used with k%-NN, it produces more accurate results than the conventional collaborative filtering technique and content-based approach.



Total Pages: 9
Pages: 261-269


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Volume: 23
Issue: 2
Year: 2016

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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