An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor
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...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020012 |
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| author | Chunxia Zhang Ming Yang Jing Lv Wanqi Yang |
| author_facet | Chunxia Zhang Ming Yang Jing Lv Wanqi Yang |
| author_sort | Chunxia Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c6e70c056a3e4924bfefc7d03677c717 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-c6e70c056a3e4924bfefc7d03677c7172025-02-02T06:00:35ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-06-011212813610.26599/BDMA.2018.9020012An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time FactorChunxia Zhang0Ming Yang1Jing Lv2Wanqi Yang3<institution content-type="dept">School of Computer Science and Technology</institution>, <institution>Nanjing Normal University</institution>, <city>Nanjing</city> <postal-code>210023</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Technology</institution>, <institution>Nanjing Normal University</institution>, <city>Nanjing</city> <postal-code>210023</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Technology</institution>, <institution>Nanjing Normal University</institution>, <city>Nanjing</city> <postal-code>210023</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Technology</institution>, <institution>Nanjing Normal University</institution>, <city>Nanjing</city> <postal-code>210023</postal-code>, <country>China</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020012recommendation systemsimilaritytagtime factor |
| spellingShingle | Chunxia Zhang Ming Yang Jing Lv Wanqi Yang An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor Big Data Mining and Analytics recommendation system similarity tag time factor |
| title | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor |
| title_full | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor |
| title_fullStr | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor |
| title_full_unstemmed | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor |
| title_short | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor |
| title_sort | improved hybrid collaborative filtering algorithm based on tags and time factor |
| topic | recommendation system similarity tag time factor |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020012 |
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