TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

MRBUP : MapReduce based scalable user profiling in recommender system

* ,

* Corresponding author · click or hover a name for details

pp. 935–945Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1617XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1617
Pages:
935–945

Abstract

The speed at which data is generated has accelerated over the years and is predicted to grow exponentially in the future. It is preferable to pique the user’s interest by building a user profile, then filter the products based on their preferences. User profiling becomes necessary as it enables the system to understand the user’s tastes and likes from voluminous data. To build a scalable system, this paper proposes MRBUP which focuses on creating scalable user profile by using the model of MapReduce. Users in a recommender system interacts with products, which are humungous in nature. To build user profile, MRBUP practices term frequency-inverse document frequency technique. For experimental analysis, this work uses the Amazon Product Reviews dataset which defines product reviews given by users to different products in the catalog. MRBUP utilizes the product reviews given by the user to the products which are in pure textual form to generate user profile. MRBUP is executed on a Hadoop cluster of varying size and computational time is observed. Scalability MRBUP is evaluated using the usual metric of Speedup factor.

Keywords

Subject Classifications

68T99

References

[1] Mekouar, Loubna, Youssef Iraqi, and Issam Damaj. “A global user profile framework for effective recommender systems.” Multimedia Tools and Applications, 1-21, (2023). DOI: https://doi.org/10.1007/s11042-023-17436-w.
[2] Gupta, Shaurya, et al. “Tier application in multi-cloud databases to improve security and service availability.” Handbook of research on cloud computing and Big Data applications in IoT. IGI Global, 82-93 (2019).
[3] Purohit, Neha, Joshi, Shubhalaxmi, Pande, Milind & Lincke, Susan. “Pragmatic analysis of ECC based security models from an empirical perspective.” Journal of Discrete Mathematical Sciences and Cryptography, 26:3, 739-758. (2023). DOI: 10.47974/JDMSC-1749.
[4] Jena, Biswaranjan, Mishra, Debahuti & Mishra, Smita Prava. “RDBMS to distributed in-memory data grid: A paradigm shift for improved performance.” Journal of Statistics and Management Systems, 26:1, 169-175, (2023). DOI: 10.47974/JSMS-956.
[5] Betancourt, Yanelys, and Sergio Ilarri. “Use of Text Mining Techniques for Recommender Systems.” In ICEIS (1), pp. 780-787. (2020). DOI: 10.5220/0009576507800787.
[6] Peterson, Jacob. “User Profiling in Recommender Systems.” (2021). DOI:https://umm-csci.github.io/senior-seminar/seminars/spring2021/peterson.pdf.
[7] Sharma, S., Raja, L., Bhatnagar, V., Sharma, D., Bhagirath, S. N., & Poonia, R. C. Hybrid HOG-SVM encrypted face detection and recognition model. Journal of Discrete Mathematical Sciences and Cryptography, 25(1), 205–218 (2022). https://doi.org/10.1080/09720529.2021.2014141.
[8] Anjali, Anjali, Jasminder Kaur Sandhu, and Deepam Goyal. “User profiling in travel recommender system using hybridization and collaborative method.” In 2021 international conference on computing, communication, and intelligent systems (ICCCIS), pp. 143-148. IEEE, (2021). DOI:10.1109/ICCCIS51004.2021.9397099.
[9] Hernandez-Bocanegra, D.C. and Ziegler, J., Explaining review-based recommendations: Effects of profile transparency, presentation style and user characteristics. i-com, 19(3), pp.181-200 (2021). DOI: https://doi.org/10.1515/icom-2020-0021.
[10] Bilal, Muhammad, et al. “Profiling users’ behavior, and identifying important features of review “helpfulness”.” IEEE Access 8 (2020): 77227-77244. DOI: 10.1109/ACCESS.2020.2989463.
[11] Liang, Huizhi. “DRprofiling: Deep reinforcement user profiling for recommendations in heterogenous information networks.” IEEE Transactions on Knowledge and Data Engineering 34.4 (2020): 1723-1734. DOI: 10.1109/TKDE.2020.2998695.
[12] Sahu, Ashish Kumar, and Pragya Dwivedi. “User profile as a bridge in cross-domain recommender systems for sparsity reduction.” Applied Intelligence 49 : 2461-2481 (2019). DOI: https://doi.org/10.1007/s10489-018-01402-3.
[13]     Zhu, Feng, et al. “Cross-domain recommendation: challenges, progress, and prospects.” arXiv preprint arXiv:2103.01696 (2021). DOI: https://doi.org/10.48550/arXiv.2103.01696.
[14] Zang, Tianzi, et al. “A survey on cross-domain recommendation: taxonomies, methods, and future directions.” ACM Transactions on Information Systems 41.2 : 1-39 (2022). DOI: https://doi.org/10.1145/3548455.
[15] Lu, Zhongqi, Sinno Jialin Pan, Yong Li, Jie Jiang, and Qiang Yang. “Collaborative evolution for user profiling in recommender systems.” In IJCAI, pp. 3804-3810 (2016). DOI: https://www.ijcai.org/Proceedings/16/Papers/535.pdf.
[16] Gu, Yulong, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. “Hierarchical user profiling for e-commerce recommender systems.” In Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 223-231 (2020). DOI: https://doi.org/10.1145/3336191.3371827.
[17] Jung, Haemin, Heesung Park, and Kwangyon Lee. “Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs.” Applied Sciences 13, no. 18 : 10041 (2023). DOI: https://doi.org/10.3390/app131810041.
[18] McAuley, Julian, and Jure Leskovec. “Hidden factors and hidden topics: understanding rating dimensions with review text.” In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172 (2013). DOI: https://doi.org/10.1145/2507157.2507163.

Views: 259Downloads: 137Citations: 0