Amine Sennouni
ABSTRACT
Discovering hidden knowledge patterns in customer behavior data can help to achieve more fitting suggestions. Nowadays, Electronic Commerce (EC) provides a new gateway for customers; and the tremendous amount of data that is shared everyday by users can be analyzed and used to predict their expectations and to fit their needs through a large variety of available products. In this paper, we developed a recommendation system which analyzes both users and products by two distinct modeling functions. This bidirectional analysis is the basis of the proposed algorithm that provides more dynamic and personalized recommendations. This algorithm uses a statistical weighting scheme and two machine learning models to predict customer's expectations and products' fitting. Then, the decision is made based on an appropriate threshold. Moreover, the proposed approach gathers the utility values of similar products to further adjust the relation users-products. Experimentations on Santander dataset highlighted the outperformance of the proposed approach compared to its counterparts in the literature.
- Pecune, F., Murali, S., Tsai, V., Matsuyama, Y., & Cassell, J. (2019, July). A Model of Social Explanations for a Conversational Movie Recommendation System. In Proceedings of the 7th International Conference on Human-Agent Interaction (pp. 135--143). ACM.Google Scholar
Digital Library
- Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12--32.Google ScholarDigital Library
- Carrer-Neto, W., Hernández-Alcaraz, M. L., Valencia-García, R., & García-Sánchez, F. (2012). Social knowledge-based recommender system. Application to the movies domain. Expert Systems with applications, 39(12), 10990--11000.Google Scholar
- Park, M. H., Hong, J. H., & Cho, S. B. (2007, July). Location-based recommendation system using bayesian user's preference model in mobile devices. In International conference on ubiquitous intelligence and computing (pp. 1130--1139). Springer, Berlin, Heidelberg.Google Scholar
- Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., & Zha, H. (2018, February). Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 108--116). ACM.Google ScholarDigital Library
- Albatayneh, N. A., Ghauth, K. I., & Chua, F. F. (2018). Utilizing learners' negative ratings in semantic content-based recommender system for e-learning forum. Journal of Educational Technology & Society, 21(1), 112--125.Google Scholar
- Cherif, W. (2018). Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. Procedia Computer Science, 127, 293--299.Google ScholarDigital Library
- Strub, F., Gaudel, R., & Mary, J. (2016, September). Hybrid recommender system based on autoencoders. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (pp. 11--16). ACM.Google ScholarDigital Library
- Logesh, R., & Subramaniyaswamy, V. (2019). Exploring hybrid recommender systems for personalized travel applications. In Cognitive informatics and soft computing (pp. 535--544). Springer, Singapore.Google ScholarCross Ref
- Puglisi, S., Parra-Arnau, J., Forné, J., & Rebollo-Monedero, D. (2015). On content-based recommendation and user privacy in social-tagging systems. Computer Standards & Interfaces, 41, 17--27.Google ScholarDigital Library
- Bocanegra, C. L. S., Ramos, J. L. S., Rizo, C., Civit, A., & Fernandez-Luque, L. (2017). HealthRecSys: A semantic content-based recommender system to complement health videos. BMC medical informatics and decision making, 17(1), 63.Google Scholar
- Wang, Y., Yin, G., Cai, Z., Dong, Y., & Dong, H. (2015). A trust-based probabilistic recommendation model for social networks. Journal of Network and Computer Applications, 55, 59--67.Google ScholarCross Ref
- Huang, Z., Shan, G., Cheng, J., & Sun, J. (2019). TRec: An efficient recommendation system for hunting passengers with deep neural networks. Neural Computing and Applications, 31(1), 209--222.Google ScholarDigital Library
- Wang, S. L., & Wu, C. Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with applications, 38(9), 10831--10838.Google Scholar
- Hwangbo, H., Kim, Y. S., & Cha, K. J. (2018). Recommendation system development for fashion retail ecommerce. Electronic Commerce Research and Applications, 28, 94--101.Google ScholarDigital Library
- Cheng, C. Y., Lin, I. C., & Wu, H. J. (2019). Recommendation System to Identify Collusive Users in Online Auctions Using the Pollution Diffusion Method. Journal of Internet Technology, 20(2), 353--358.Google Scholar
- Jothi, B., & Pushpalatha, M. (2018). Recommendation System-Item User Matrix: A Graph Base System. Journal of Computational and Theoretical Nanoscience, 15(9-10), 3044--3048.Google ScholarCross Ref
- Lopes, P., & Roy, B. (2015). Dynamic recommendation system using web usage mining for ecommerce users. Procedia Computer Science, 45, 60--69.Google ScholarCross Ref
- Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia tools and applications, 77(4), 4379--4399.Google Scholar
- Wang, S., Lo, D., Vasilescu, B., & Serebrenik, A. (2018). EnTagRec++: An enhanced tag recommendation system for software information sites. Empirical Software Engineering, 23(2), 800--832.Google ScholarDigital Library
- Kim, S., & Kwon, J. (2007). Effective context-aware recommendation on the semantic web. International Journal of Computer Science and Network Security, 7(8), 154--159.Google Scholar
- Golder, S., & Huberman, B. A. (2005). The structure of collaborative tagging systems. arXiv preprint cs/0508082.Google Scholar
- Patil, A. E., Patil, S., Singh, K., Saraiya, P., & Sheregar, A. (2019). Online book recommendation system using association rule mining and collaborative filtering, 8(4), 83--87.Google Scholar
- Tsaku, N. Z., & Kosaraju, S. (2019, April). Boosting Recommendation Systems through an Offline Machine Learning Evaluation Approach. In Proceedings of the 2019 ACM Southeast Conference (pp. 182--185). ACM.Google ScholarDigital Library
- Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P., Nielek, R., & Rutkowski, L. (2018, July). A content-based recommendation system using neuro-fuzzy approach. In 2018 IEEE International Conference on Fuzzy Systems (fuzz-ieee) (pp. 1--8). IEEE.Google ScholarDigital Library
- Xia, B., Ni, Z., Li, T., Li, Q., & Zhou, Q. (2017). Vrer: context-based venue recommendation using embedded space ranking SVM in location-based social network. Expert Systems with Applications, 83, 18--29.Google ScholarDigital Library
- Vapnik, V., & Mukherjee, S. (2000). Support vector method for multivariate density estimation. In Advances in neural information processing systems, 659--665.Google Scholar
- Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3), 27.Google ScholarDigital Library
- Labjar, H., Cherif, W., Nadir, S., Digua, K., Sallek, B., & Chaair, H. (2016). Support vector machines for modelling phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and phosphoric acid solution. Journal of Taibah University for Science, 10(5), 745--754.Google ScholarCross Ref
- Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.Google ScholarCross Ref
- Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293--300.Google Scholar
- Smits, G. F., & Jordaan, E. M. (2002). Improved SVM regression using mixtures of kernels. In Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on (Vol. 3, pp. 2785--2790). IEEE.Google ScholarCross Ref
- Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS one, 10(3), e0118432.Google ScholarCross Ref
- Morstatter, F., Wu, L., Nazer, T. H., Carley, K. M., & Liu, H. (2016, August). A new approach to bot detection: striking the balance between precision and recall. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 533--540). IEEE.Google ScholarCross Ref
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