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

Show Author's information Chunxia ZhangMing Yang( )Jing LvWanqi Yang
School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China.

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.

Keywords:

recommendation system, similarity, tag, time factor
Received: 27 December 2017 Accepted: 03 January 2018 Published: 12 April 2018 Issue date: June 2018
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Publication history

Received: 27 December 2017
Accepted: 03 January 2018
Published: 12 April 2018
Issue date: June 2018

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© The author(s) 2018

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61432008 and 61272222).

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