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Open Access

An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor

School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China.
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Abstract

The Collaborative Filtering (CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems (RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor (TT-HybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy.

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Big Data Mining and Analytics
Pages 128-136
Cite this article:
Zhang C, Yang M, Lv J, et al. An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor. Big Data Mining and Analytics, 2018, 1(2): 128-136. https://doi.org/10.26599/BDMA.2018.9020012

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Received: 27 December 2017
Accepted: 03 January 2018
Published: 12 April 2018
© The author(s) 2018
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