ICTACT Journal on Soft Computing

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User Activities Analysis in Location Based Social Network Via Association Rules


Affiliations
1 Department of Computer Science, Ferhat Abbas University, Algeria
2 Department of Computer Science, Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, Algeria
3 State University of New York Polytechnic Institute, United States
     

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In recent years, the field of the Internet of Things (IoT), including smart and wearable devices, has witnessed a tremendous advancement leading to the collection of a wide variety of information not only about users but also their activities via various systems such as social networks, apps and so on. Thus, the collection of this large amount of data allows social systems to reach a wide variety of targets and gives more visibility about users and their profiles. It can also help to improve the services and functionalities of the users. Besides, the analysis and prediction of user’s activities in location-based social networks (LBSNs) have received much attention both from industries and research communities, especially in smart city developments, which give much importance to the automation of the LBSNs. In this paper, we present a new method based on association rules for user activity analysis in LBSNs. In particular, the Apriori algorithm has been applied to extract the consequential and advantageous rules to categorize users’ profiles. Empirical evaluations on a publicly available large-scale real-world dataset, named Gowalla, demonstrate the effectiveness of the presented association rules-based system in analyzing users’ activities via LBSNs.

Keywords

Complex System, Social Networks, Association Rules, Apriori Algorithm, Gowalla Dataset.
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  • M. Ye, P. Yin, W.C. Lee and D.L. Lee, “Exploiting Geographical influence for Collaborative Point-of-Interest Recommendation”, Proceedings of International ACM SIGIR Conference on Research and development in Information Retrieval, pp. 325-334, 2011.

  • C. Yang, M. Sun, W.X. Zhao, Z. Liu and E.Y. Chang, “A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories”, ACM Transactions on Information Systems, Vol. 35, No. 4, pp. 1-28, 2017.

  • J. Bao, Y. Zheng, D. Wilkie and M. Mokbel, “Recommendations in Location-Based Social Networks: A Survey”, Geoinformatica, Vol. 19, No. 3, pp.525-565, 2015.

  • H. Gao, J. Tang, X. Hu and H. Liu, “Content-Aware Point of Interest Recommendation on Location-Based Social Networks.,” Aaai, Vol. 15, pp. 1721-1727, 2015.

  • Y. Qiao, X. Luo, C. Li, H. Tian and J. Ma, “Heterogeneous Graph-Based Joint Representation Learning for Users and POIs in Location-Based Social Network”, Information Processing and Management, Vol. 57, No. 2, p. 102151-102163, 2020.

  • W.X. Zhao, F. Fan, J.R. Wen and E.Y. Chang, “Joint Representation Learning for Location-Based Social Networks with Multi-Grained Sequential Contexts”, ACM Transactions on Knowledge Discovery from Data, Vol. 12, No. 2, pp. 1-21, 2018.

  • S. Feng, X. Li, Y. Zeng, G. Cong and Y.M. Chee, “Personalized Ranking Metric Embedding for Next New Poi Recommendation”, Proceedings of International Conference on Artificial Intelligence, pp. 2069-2075, 2015.

  • X. Chen, Y. Zeng, G. Cong, S. Qin, Y. Xiang and Y. Dai, “On Information Coverage for Location Category based Point-of-Interest Recommendation”, Proceedings of AAAI Conference on Artificial Intelligence, pp. 1-8, 2015.

  • H. Li, Y. Ge, R. Hong and H. Zhu, “Point-of-Interest Recommendations: Learning Potential Check-Ins from Friends”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 975-984, 2016.

  • D. To, D. Si and Y. Chen, “Traveler’s Next Activity Predication with Location-Based Social Network Data”, Proceedings of International Workshop on Prediction of Human Mobility, pp. 15-23, 2019.

  • A. Likhyani, D. Padmanabhan, S. Bedathur and S. Mehta, “Inferring and Exploiting Categories for Next Location Prediction”, Proceedings of International Conference on World Wide Web, pp. 65-66, 2015.

  • I. B. Shem Tov and S. Bekhor, “Extracting Travel Demand for Emergency Situations using Location-Based Social Network Data”, Transportation Research Procedia, Vol. 45, pp. 111-118, 2020.

  • X. He, L. Liao, H. Zhang, L. Nie, X. Hu and T.S. Chua, “Neural Collaborative Filtering”, Proceedings of International Conference on World Wide Web, pp. 173-182, 2017.

  • C.C. Aggarwal, “Data Mining: The Textbook”, Springer, 2015.

  • P. Ristoski and H. Paulheim, “Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey”, Journal of Web Semantics, Vol. 36, pp. 1-22, 2016.

  • R. Agrawal, T. Imielinski and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases”, Proceedings of International Conference on Management of Data, pp. 207-216, 1993.

  • R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules”, Proceedings of International Conference on Very Large Data Bases, pp. 487-499, 1994.

  • Z. Zou, X. Xie and C. Sha, “Mining User Behavior and Similarity in Location-based Social Networks”, Proceedings of International Conference on Parallel Architectures, Algorithms and Programming, pp. 167-171, 2015.

  • Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang and S. Y. Philip, “A Comprehensive Survey on Graph Neural Networks”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 1, pp. 4-24, 2020.

  • F. Hao, J. Zhang, Z. Duan, L. Zhao, L. Guo and D.S. Park, “Urban Area Function Zoning Based on User Relationships in Location-Based Social Networks,” IEEE Access, Vol. 8, pp. 23487-23495, 2020.

  • F. Berzal, I. Blanco, D. Sanchez and M.A. Vila, “A New Framework to Assess Association Rules”, Proceedings of International Symposium on Intelligent Data Analysis, pp. 95-104, 2001.

  • M. Hahsler, “A Rules Viz: Interactive Visualization of Association Rules with R”, The R Journal, Vol. 9, No. 2, pp. 1-13, 2017.

  • B.V Almende and B. Thieurmel, “Vis Network: Network Visualization using ‘Vis. Js’ Library”, Available at https://cran.r-project.org/web/packages/visNetwork/index.html, Accessed at 2017.

  • L. Yang, “Visualizing Frequent Item Sets, Association Rules, and Sequential Patterns in Parallel Coordinates”, Proceedings of International Conference on Computational Science and Its Applications, pp. 21-30, 2003.


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  • User Activities Analysis in Location Based Social Network Via Association Rules

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Authors

Yahia Belayadi
Department of Computer Science, Ferhat Abbas University, Algeria
Abdallah Khababa
Department of Computer Science, Ferhat Abbas University, Algeria
Abdelouahab Attia
Department of Computer Science, Mohamed El-Bachir Ibrahimi University of Bordj Bou Arreridj, Algeria
Zahid Akhtar
State University of New York Polytechnic Institute, United States

Abstract


In recent years, the field of the Internet of Things (IoT), including smart and wearable devices, has witnessed a tremendous advancement leading to the collection of a wide variety of information not only about users but also their activities via various systems such as social networks, apps and so on. Thus, the collection of this large amount of data allows social systems to reach a wide variety of targets and gives more visibility about users and their profiles. It can also help to improve the services and functionalities of the users. Besides, the analysis and prediction of user’s activities in location-based social networks (LBSNs) have received much attention both from industries and research communities, especially in smart city developments, which give much importance to the automation of the LBSNs. In this paper, we present a new method based on association rules for user activity analysis in LBSNs. In particular, the Apriori algorithm has been applied to extract the consequential and advantageous rules to categorize users’ profiles. Empirical evaluations on a publicly available large-scale real-world dataset, named Gowalla, demonstrate the effectiveness of the presented association rules-based system in analyzing users’ activities via LBSNs.

Keywords


Complex System, Social Networks, Association Rules, Apriori Algorithm, Gowalla Dataset.

References