ORIGINAL ARTICLE
An E-commerce prediction system for product allocation to bridge the gap between cultural analytics and data science
Shefali Singhal,
Poonam Tanwar,
Corresponding Author
Shefali Singhal
Computer Science & Engineering Department, Manav Rachna International Institute of Research and Studies, Faridabad, India
Correspondence
Shefali Singhal, Computer Science & Engineering Department, Manav Rachna International Institute of Research and Studies, Sector-43, Surajkund Road, Faridabad, India.
Email: [email protected]
Search for more papers by this authorPoonam Tanwar
Computer Science & Engineering Department, Manav Rachna International Institute of Research and Studies, Faridabad, India
Search for more papers by this authorShefali Singhal,
Poonam Tanwar,
Corresponding Author
Shefali Singhal
Computer Science & Engineering Department, Manav Rachna International Institute of Research and Studies, Faridabad, India
Correspondence
Shefali Singhal, Computer Science & Engineering Department, Manav Rachna International Institute of Research and Studies, Sector-43, Surajkund Road, Faridabad, India.
Email: [email protected]
Search for more papers by this authorPoonam Tanwar
Computer Science & Engineering Department, Manav Rachna International Institute of Research and Studies, Faridabad, India
Search for more papers by this authorAbstract
With the emerging era of E-commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
REFERENCES
- Alonso-Misol Gerlache, H., Moreno-Ger, P., & De-La-Fuente Valentín, L. (2021). Towards the Grade's prediction. A study of different machine learning approaches to predict grades from student interaction data. International Journal Of Interactive Multimedia And Artificial Intelligence, 7, 1–9. http://doi.org/10.9781/ijimai.2021.11.007
- Bacigalupo, D. A., Jarvis, S. A., He, L., Spooner, D. P., Dillenberger, D. N., & Nudd, G. R. (2005). An investigation into the application of different performance prediction methods to distributed enterprise applications. The Journal of Supercomputing, 34(2), 93–111. https://doi.org/10.1007/s11227-005-2335-z
- Baldominos Gómez, A., Saez, Y., Quintana, D., & Isasi, P. (2022). AWS PredSpot: Machine learning for predicting the Price of spot instances in AWS cloud. International Journal Of Interactive Multimedia And Artificial Intelligence, 7, 65–74.
- Chatterjee, I. (2021). Artificial intelligence and patentability: Review and discussions. International Journal of Modern Research, 1(1), 15–21.
- Dehghani, M., Montazeri, Z., Dehghani, A., Ramirez-Mendoza, R. A., Samet, H., Guerrero, J. M., & Dhiman, G. (2020). MLO: Multi leader optimizer. International Journal of Intelligent Engineering and Systems, 13, 364–373. https://doi.org/10.22266/ijies2020.1231.32
10.22266/ijies2020.1231.32 Google Scholar
- Dehghani, M., Montazeri, Z., Dhiman, G., Malik, O. P., Morales-Menendez, R., Ramirez-Mendoza, R. A., & Parra-Arroyo, L. (2020). A spring search algorithm applied to engineering optimization problems. Applied Sciences, 10(18), 6173.
- Dehghani, M., Montazeri, Z., Givi, H., Guerrero, J. M., & Dhiman, G. (2020). Darts game optimizer: A new optimization technique based on darts game. International Journal of Intelligent Engineering and Systems, 13(5), 286–294. https://doi.org/10.22266/ijies2020.1031.26
10.22266/ijies2020.1031.26 Google Scholar
- Dehghani, M., Montazeri, Z., Malik, O. P., Dhiman, G., & Kumar, V. (2019). BOSA: Binary orientation search algorithm. International Journal of Innovative Technology and Exploring Engineering, 9(1), 5306–5310. https://doi.org/10.35940/ijitee.A4215.119119
10.35940/ijitee.A4215.119119 Google Scholar
- Dhiman, G. (2021). ESA: A hybrid bio-inspired metaheuristic optimization approach for engineering problems. Engineering with Computers, 37(1), 323–353.
- Dhiman, G., Garg, M., Nagar, A., Kumar, V., & Dehghani, M. (2021). A novel algorithm for global optimization: Rat swarm optimizer. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8457–8482.
- Dhiman, G., & Kaur, A. (2019). STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148–174.
- Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48–70.
- Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50.
- Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169–196.
- Dhiman, G., Oliva, D., Kaur, A., Singh, K. K., Vimal, S., Sharma, A., & Cengiz, K. (2021). BEPO: A novel binary emperor penguin optimizer for automatic feature selection. Knowledge-Based Systems, 211, 106560.
- Dong, Y., & Jiang, W. (2019). Brand purchase prediction based on time-evolving user behaviors in e-commerce. Concurrency and Computation: Practice and Experience, 31(1), e4882. https://doi.org/10.1002/cpe.4882
- Ejiyi, C. J., Qin, Z., Salako, A. A., Happy, M. N., Nneji, G. U., Ukwuoma, C. C., et al. (2022). Comparative analysis of building insurance prediction using some machine learning algorithms. International Journal Of Interactive Multimedia And Artificial Intelligence, 7, 75–85.
- Fabra, J., Álvarez, P., & Ezpeleta, J. (2020). Log-based session profiling and online behavioral prediction in E–commerce websites. IEEE Access, 8, 171834–171850. https://doi.org/10.1109/ACCESS.2020.3024649
- Frémal, S., & Lecron, F. (2017). Weighting strategies for a recommender system using item clustering based on genres. Expert Systems with Applications, 77, 105–113. https://doi.org/10.1016/j.eswa.2017.01.031
- Gao, D., Yang, W., Zhou, H., Wei, Y., Hu, Y., & Wang, H. (2020). Deep hierarchical classification for category prediction in e-commerce system. arXiv preprint arXiv:2005.06692. https://arxiv.org/abs/2005.06692
- Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018, April). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics: Conference Series, 1000(1), 012101.
10.1088/1742-6596/1000/1/012101 Google Scholar
- Gupta, V. K., Shukla, S. K., & Rawat, R. S. (2022). Crime tracking system and people's safety in India using machine learning approaches. International Journal of Modern Research, 2(1), 1–7.
- Han, J., Kamber, M., & Pei, J. (2012). Advanced cluster analysis. Data Mining: Concepts and Techniques, 3, 501–508.
- Hong, X., Wang, J., & Qi, G. (2014). Comparison of spectral clustering, K-clustering and hierarchical clustering on e-nose datasets: Application to the recognition of material freshness, adulteration levels and pretreatment approaches for tomato juices. Chemometrics and Intelligent Laboratory Systems, 133, 17–24.
- Jia, R., Li, R., Yu, M., & Wang, S. (2017, July). E-commerce purchase prediction approach by user behavior data. In 2017 international conference on computer, information and telecommunication systems (CITS) (pp. 1–5). IEEE. https://doi.org/10.1109/CITS.2017.8035294
10.1109/CITS.2017.8035294 Google Scholar
- Jiang, P., Zhu, Y., Zhang, Y., & Yuan, Q. (2015). Life-stage prediction for product recommendation in e-commerce. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1879–1888). Association for Computing Machinery. https://doi.org/10.1145/2783258.2788562
10.1145/2783258.2788562 Google Scholar
- Katarya, R., & Verma, O. P. (2017). An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal, 18(2), 105–112. https://doi.org/10.1016/j.eij.2016.10.002
- Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020a). Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.
- Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020b). Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.
- Kumar, R., & Dhiman, G. (2021). A comparative study of fuzzy optimization through fuzzy number. International Journal of Modern Research, 1(1), 1–14.
- Lee, S., Lee, S., & Park, Y. (2007). A prediction model for success of services in e-commerce using decision tree: E-customer's attitude towards online service. Expert Systems with Applications, 33(3), 572–581. https://doi.org/10.1016/j.eswa.2006.06.005
- Liu, C. J., Huang, T. S., Ho, P. T., Huang, J. C., & Hsieh, C. T. (2020). Machine learning-based e-commerce platform repurchase customer prediction model. PLoS One, 15(12), e0243105.
- Logesh, R., Subramaniyaswamy, V., Malathi, D., Sivaramakrishnan, N., & Vijayakumar, V. (2020). Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Computing and Applications, 32(7), 2141–2164. https://doi.org/10.1007/s00521-018-3891-5
- Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., & Li, X. (2019). Efficient user profiling based intelligent travel recommender system for individual and group of users. Mobile Networks and Applications, 24(3), 1018–1033. https://doi.org/10.1007/s11036-018-1059-2
- Mohamed, M. H., Khafagy, M. H., & Ibrahim, M. H. (2019). Recommender systems challenges and solutions survey. In 2019 international conference on innovative trends in computer engineering (ITCE) (pp. 149–155). IEEE. https://doi.org/10.1109/ITCE.2019.8646645
10.1109/ITCE.2019.8646645 Google Scholar
- Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507–520. https://doi.org/10.1016/j.eswa.2017.09.058
- Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. D. F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLoS One, 14(1), e0210236.
- Sharma, T., Nair, R., & Gomathi, S. (2022). Breast cancer image classification using transfer learning and convolutional neural network. International Journal of Modern Research, 2(1), 8–16.
- Shukla, S. K., Gupta, V. K., Joshi, K., Gupta, A., & Singh, M. K. (2022). Self-aware execution environment model (SAE2) for the performance improvement of multicore systems. International Journal of Modern Research, 2(1), 17–27.
- Singhal, S., & Tanwar, P. (2020a). Analysis of classification algorithms compatible with E-commerce technology. International Journal of Advanced Science and Technology, 29(3), 14329.
- Singhal, S., & Tanwar, P. (2020b). Machine learning techniques and their problem-specific domain based excellence. Journal of Critical Reviews, 7(6), 1841–1850. https://doi.org/10.31838/jcr.07.06.283
10.31838/jcr.07.06.283 Google Scholar
- Singhal, S., & Tanwar, P. (2021a). A prediction model for benefitting e-commerce through usage of regional data: A new framework. IAES International Journal of Artificial Intelligence, 10(4), 1009. https://doi.org/10.11591/ijai.v10.i4.pp1009-1018
10.11591/ijai.v10.i4.pp1009-1018 Google Scholar
- Singhal, S., & Tanwar, P. (2021b). Novel structure for E-business through dynamic web page visualization. Recent Patents on Engineering, 15(5), 108–113. https://doi.org/10.2174/1872212114999201109204536
10.2174/1872212114999201109204536 Google Scholar
- Su, X., Sperlì, G., Moscato, V., Picariello, A., Esposito, C., & Choi, C. (2019). An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Transactions on Industrial Informatics, 15(7), 4266–4275. https://doi.org/10.1109/TII.2019.2908056
- Tang, J., Du, X., He, X., Yuan, F., Tian, Q., & Chua, T. S. (2019). Adversarial training towards robust multimedia recommender system. IEEE Transactions on Knowledge and Data Engineering, 32(5), 855–867. https://doi.org/10.1109/TKDE.2019.2893638
- Tarus, J. K., Niu, Z., & Kalui, D. (2018). A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Computing, 22(8), 2449–2461. https://doi.org/10.1007/s00500-017-2720-6
- Tseng, K. K., Lin, R. F. Y., Zhou, H., Kurniajaya, K. J., & Li, Q. (2018). Price prediction of e-commerce products through internet sentiment analysis. Electronic Commerce Research, 18(1), 65–88. https://doi.org/10.1007/s10660-017-9272-9
- Tsurel, D., Doron, M., Nus, A., Dagan, A., Guy, I., & Shahaf, D. (2020). E-commerce dispute resolution prediction. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 1465–1474). Association for Computing Machinery. https://doi.org/10.1145/3340531.3411906
10.1145/3340531.3411906 Google Scholar
- Vaishnav, P. K., Sharma, S., & Sharma, P. (2021). Analytical review analysis for screening COVID-19 disease. International Journal of Modern Research, 1(1), 22–29.
- Xu, H., & Deng, Y. (2017). Dependent evidence combination based on shearman coefficient and Pearson coefficient. IEEE Access, 6, 11634–11640. https://doi.org/10.1109/ACCESS.2017.2783320
- Yan, Y., Xie, H., & Ma, L. (2020). Rating-based collaborative filtering using spectral clustering algorithm. Journal of Physics: Conference Series. IOP Publishing, 1549(3), 032022.
10.1088/1742-6596/1549/3/032022 Google Scholar
- Zhu, X., Gan, J., Lu, G., Li, J., & Zhang, S. (2020). Spectral clustering via half-quadratic optimization. World Wide Web, 23(3), 1969–1988. https://doi.org/10.1007/s11280-019-00731-8
