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Technical gazette, Vol. 32 No. 2, 2025.

Original scientific paper

https://doi.org/10.17559/TV-20240422001482

A Time-Location Theme Mining Algorithm Based on R-Tree and User Attention for Personalized Recommendation

Jing Yu ; School of Management, Jiujiang University, No. 551, Qianjin East Road, Jiujiang, Jiangxi 332005, China
Zhixing Lu ; Faculty of Computer Science and Information Technology, University Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
Xianghua Li ; School of Humanities, Anqing Normal University, No. 128, Linghu South Road, Anqing, Anhui 246002, China *
Shunli Zhang ; School of Computer and Information Science, Qinghai Institute of Technology, No. 2, Xiuyuan Street, Xining, Qinghai 810016, China
Bin Wu ; School of Computer and Big Data Science, Jiujiang University, No. 551, Qianjin East Road, Jiujiang, Jiangxi 332005, China
Zongmin Cui ; School of Computer and Big Data Science, Jiujiang University, No. 551, Qianjin East Road, Jiujiang, Jiangxi 332005, China

* Corresponding author.

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Abstract

This paper addresses the limitations of existing theme mining algorithms in extracting user-preferred themes from time-location data for personalized recommendation. We propose a Time-Location Theme Mining algorithm based on R-tree and user Attention (named as TLTMRA). TLTMRA combines mesh and R-Tree structures for efficient theme data processing and considers both the overall importance of themes and user attention to theme objects. Experimental results on real-world datasets demonstrate that TLTMRA outperforms state-of-the-art methods in terms of storage overhead, theme validity, and recommendation efficiency. The proposed algorithm achieves up to 59% theme validity and significantly reduces storage and computation costs compared to baseline methods. This work contributes to the development of effective and efficient personalized recommendation systems leveraging time-location data. Future research directions include extending the proposed method to other data types and recommendation scenarios and further optimizing the algorithm for large-scale applications.

Keywords

personalized recommendation; theme mining algorithm; time-location data; user-preferred themes

Hrčak ID:

328634

URI

https://hrcak.srce.hr/328634

Publication date:

27.2.2025.

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