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Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

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Abstract

Recommendation systems are now inherent for many business applications to take important business decisions. These systems are built based on the historical data that may be the sales data or customer feedback etc. Customer feedback is very important for any organization as it reflects the view, sentiment of the customers. Online systems allow customers to purchase products at a glance from any e-commerce website. Generally, the potential buyers check the review of the products to take informed decision of purchase. In this work, we attempt to build a recommendation model to find out the influence of a product on another product so that if a user purchases the influential product then the recommender system can recommend the influenced products to the users. In this paper, the recommendation system has been built based on association rule mining. We proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for association rule mining. The entire framework has been developed in Neo4j graph data model for doing the data modelling from raw text file and also to perform the analysis. We used real-life customer feedback data of amazon for experimental purpose.

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References

  1. S Shaikh, S Rathi, P Janrao. Recommendation System in E-Commerce Websites: A Graph Based Approached. 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017; pp. 931-934, doi: https://doi.org/10.1109/IACC.2017.0189

  2. A. Juneja, N. N. Das. Big Data Quality Framework: Pre-Processing Data in Weather Monitoring Application. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019; pp. 559–563, doi: https://doi.org/10.1109/COMITCon.2019.8862267.

  3. J. Patel. An Effective and Scalable Data Modeling for Enterprise Big Data Platform. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019; pp. 2691–2697, doi: https://doi.org/10.1109/BigData47090.2019.9005614.

  4. ΙοannisSchoinas, Christos Tjortjis. MuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Hersonissos, Greece. 2019; 660–672 doi: https://doi.org/10.1007/978-3-030-19823-7_55

  5. Soor GK, Morje A, Dalal R, Vora D. Product recommendation system based on user trustworthiness and sentiment analysis. ITM Web of Conf EDP Sci. 2020;32:03030. https://doi.org/10.1051/itmconf/20203203030.

    Article  Google Scholar 

  6. Wang S. Research of shopping recommendation system based on improved wide-depth network. Mater Sci Eng Conf Ser. 2020;768(7):72072. https://doi.org/10.1088/1757-899X/768/7/072072.

    Article  Google Scholar 

  7. Al-Ghuribi SM, Mohd Noah SA. Multi-criteria review-based recommender system-the state of the art. IEEE Access. 2019;7:169446–68. https://doi.org/10.1109/ACCESS.2019.2954861.

    Article  Google Scholar 

  8. Aciar SV, Aciar GI, Collazos CA, González CS. User recommender system based on knowledge, availability, and reputation from interactions in forums. IEEE RevistaIberoamericana de Tecnologias del Aprendizaje. 2016;11(1):18–22. https://doi.org/10.1109/RITA.2016.2518441.

    Article  Google Scholar 

  9. Wu D, Lu J, Zhang G. A fuzzy tree matching-based personalized e-learning recommender system. IEEE Trans Fuzzy Syst. 2015;23(6):2412–26. https://doi.org/10.1109/TFUZZ.2015.2426201.

    Article  Google Scholar 

  10. Ghosh S, Roy S, Sen S. An efficient recommendation system on e-learning platform by query lattice optimization. In: Sharma N, Chakrabarti A, Balas V, Martinovic J, editors. Data management analytics and innovation advances in intelligent systems and computing. Singapore: Springer; 2021. https://doi.org/10.1007/978-981-15-5616-6_6.

    Chapter  Google Scholar 

  11. R Ganguli, A Mehta, NC Debnath, S Aljahdali, S Sen. An Integrated Framework for Friend Recommender System Using Graph Theoretic Approach. Proc. of the 35th International Conference on Computers and Their Applications (CATA 2020) EPiC Series in Computing 2020; 69:242–255. DOI: https://doi.org/10.29007/4bwn

  12. Yuan NJ, Zheng Y, Zhang L, Xie X. T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans Knowl Data Eng. 2013;25(10):2390–403. https://doi.org/10.1109/TKDE.2012.153.

    Article  Google Scholar 

  13. Pinto T, Faia R, Navarro-Caceres M, Santos G, Corchado JM, Vale Z. Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings. IEEE Syst J. 2019;13(1):1084–95. https://doi.org/10.1109/JSYST.2018.2876933.

    Article  Google Scholar 

  14. Czibula G, Czibula I, Miholca D, Crivei L. A novel concurrent relational association rule mining approach. Expert Syst Appl. 2019. https://doi.org/10.1016/j.eswa.2019.01.082.

    Article  Google Scholar 

  15. Vougas K, Sakellaropoulos T, Kotsinas A, Foukas GRP, Ntargaras A, Koinis F, Georgoulias V. Machine learning and data mining frameworks for predicting drug response in cancer: an overview and a novel in silico screening process based on association rule mining. Pharmacol Ther. 2019;203:107395. https://doi.org/10.1016/j.pharmthera.2019.107395.

    Article  Google Scholar 

  16. Yan D, Zhao X, Lin R, et al. PPQAR: Parallel PSO for quantitative association rule mining. Peer-to-Peer Netw Appl. 2019;12:1433–44. https://doi.org/10.1007/s12083-018-0698-1.

    Article  Google Scholar 

  17. Maji G, Sen S, Sarkar A. Share Market Sectoral Indices Movement Forecast with Lagged Correlation and Association Rule Mining. In: Saeed K, Homenda W, Chaki R, editors. Computer Information Systems and Industrial Management CISIM 2017 Lecture Notes in Computer Science. Cham: Springer; 2017. https://doi.org/10.1007/978-3-319-59105-6_28.

    Chapter  Google Scholar 

  18. S Roy, B Shit, S Sen. Association Based Multi-Attribute Analysis to Construct Materialized View. Springer 3rd International Doctoral Symposium on Applied Computations and Security Systems (ACSS 2016) Aug 12-14, Kolkata, India. Advances in Intelligent Systems and Computing book series (AISC, volume 567) pp 115-131; ISBN: 978-981-10-3409-1; https://doi.org/https://doi.org/10.1007/978-981-10-3409-1_8

  19. Mallik S, Mukhopadhyay A, Maulik U. RANWAR: rank-based weighted association rule mining from gene expression and methylation data. IEEE Trans NanoBiosci. 2015;14(1):59–66. https://doi.org/10.1109/TNB.2014.2359494.

    Article  Google Scholar 

  20. Jin H, Chen J, He H, Williams GJ, Kelman C, O’Keefe CM. Mining unexpected temporal associations: applications in detecting adverse drug reactions. IEEE Trans Inf Technol Biomed. 2008;12(4):488–500. https://doi.org/10.1109/TITB.2007.900808.

    Article  Google Scholar 

  21. Luna JM, Padillo F, Pechenizkiy M, Ventura S. Apriori versions based on mapreduce for mining frequent patterns on big data. IEEE Trans Cybern. 2018;48(10):2851–65. https://doi.org/10.1109/TCYB.2017.2751081.

    Article  Google Scholar 

  22. Sheng G, Hou H, Jiang X, Chen Y. A novel association rule mining method of big data for power transformers state parameters based on probabilistic graph model. IEEE Trans Smart Grid. 2018;9(2):695–702. https://doi.org/10.1109/TSG.2016.2562123.

    Article  Google Scholar 

  23. JamshidiNejad S, Ahmadi-Abkenari F, Bayat P. A combination of frequent pattern mining and graph traversal approaches for aspect elicitation in customer reviews. IEEE Access. 2020;8:151908–25. https://doi.org/10.1109/ACCESS.2020.3017486.

    Article  Google Scholar 

  24. C Cattuto, M Quaggiotto, A Panisson, A Averbuch. Time-varying social networks in a graph database: a Neo4j use case. In First International Workshop on Graph Data Management Experiences and Systems (GRADES '13). Association for Computing Machinery, New York, NY, USA, Article 11, 2013; 1–6. DOI: https://doi.org/10.1145/2484425.2484442

  25. Georgios D, Andreas K, Phivos M, Spyros S. Defining and evaluating twitter influence metrics: a higher order approach in Neo4j. Soc Netw Anal Min (SNAM). 2017. https://doi.org/10.1007/s13278-017-0467-9.

    Article  Google Scholar 

  26. Tsoulias K, Palaiokrassas G, Fragkos G, Litke A, Varvarigou TA. A graph model based blockchain implementation for increasing performance and security in decentralized ledger systems. IEEE Access. 2020;8:130952–65. https://doi.org/10.1109/ACCESS.2020.3006383.

    Article  Google Scholar 

  27. INPW Dharmawan, R Sarno. Book recommendation using Neo4j graph database in BibTeX book metadata. 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, 2017; 47–52 doi: https://doi.org/10.1109/ICSITech.2017.8257084.

  28. Stothers JAM, Nguyen A. Can Neo4j replace PostgreSQL in Healthcare? AMIA Jt Summits Transl Sci Proc. 2020;2020:646–53.

    Google Scholar 

  29. Ni J, Li J, McAuley J. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019;188–197). DOI: https://doi.org/10.18653/v1/D19-1018

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Authors and Affiliations

  1. A.K. Choudhury School of I.T, University of Calcutta, JD-2, Sector-3 Saltlake, Kolkata, 700106, West Bengal, India

    Sudipta Sen & Soumya Sen

  2. Department of Computer Science, The Bhawanipur Education Society College, 5, L.L.R Sarani, Kolkata, 700020, West Bengal, India

    Akash Mehta & Runa Ganguli

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  1. Sudipta Sen
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  2. Akash Mehta
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  3. Runa Ganguli
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  4. Soumya Sen
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Correspondence to Soumya Sen.

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This article is part of the topical collection “Applications of Software Engineering and Tool Support” guest edited by Nabendu Chaki, Agostino Cortesi and Anirban Sarkar.

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Sen, S., Mehta, A., Ganguli, R. et al. Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study. SN COMPUT. SCI. 2, 74 (2021). https://doi.org/10.1007/s42979-021-00460-8

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