AgentG: A user friendly and engaging bot to chat for e-commerce lovers
Keywords:Deep Neural Network, Keras, Chatbot, NLTK, E-commerce
Regular customer assistance chatbots are generally based on dialogues delivered by humans. It faces symbolic issues usually related to data scaling and the privacy of one’s information. In this paper, we coeval AgentG, an intelligent chatbot used for customer assistance. It is built using deep neural network architecture. It clouts huge-scale and free publicly accessible e-commerce data. Different from existing counterparts, AgentG takes a great data advantage from in-pages that contain product descriptions along with user-generated data content from these online eCommerce websites. It results in more efficient from a practical point of view as well as cost-effective while answering questions that are repetitive. This helps in providing the freedom to people who work as customer service in order to answer questions with highly accurate answers. We have demonstrated how AgentG acts as an additional extension to the actual stream web browsers and how it is useful to users in having a better experience who are doing online shopping.
Baktha, K. & Tripathy, B.K. (2017, April). Investigation of recurrent neural networks in the field of sentiment analysis. In 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 2047-2050, IEEE. IEEE. https://doi.org/10.1109/ICCSP.2017.8286763
Behera, B. (2016). Chappie-a semi-automatic intelligent chatbot. Write-Up. Kowalski, S., Pavlovska, K. and Goldstein, M., 2009, July. Two case studies in using chatbots for security training. In IFIP World Conference on Information Security Education (pp. 265-272). Springer, Berlin, Heidelberg.
Cui, L., Huang, S., Wei, F., Tan, C., Duan, C. & Zhou, M. (2017). SuperAgent: A customer service chatbot for e-commerce websites. Proceedings of ACL 2017, System Demonstrations, pp.97-102. https://doi.org/10.18653/v1/P17-4017
Du Preez, S.J., Lall, M. & Sinha, S. (2009, May). An intelligent web-based voice chat bot. In IEEE EUROCON 2009, pp. 386-391, IEEE. https://doi.org/10.1109/EURCON.2009.5167660
El Zini, J., Rizk, Y., Awad, M. & Antoun, J. (2019, July). Towards A Deep Learning Question-Answering Specialized Chatbot for Objective Structured Clinical Examinations. In 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-9, IEEE. https://doi.org/10.1109/IJCNN.2019.8851729
Gupta, A. & Tripathy, B.K. (2014, February). A generic hybrid recommender system based on neural networks. In 2014 IEEE International Advance Computing Conference (IACC), pp. 1248-1252, IEEE. ttps://doi.org/10.1109/IAdCC.2014.6779506
Holotescu, C. (2016). MOOCBuddy: a Chatbot for personalized learning with MOOCs. In RoCHI, pp. 91-94.
Hristidis, V. (2018, September). Chatbot Technologies and Challenges. In 2018 First International Conference on Artificial Intelligence for Industries (AI4I), pp. 126-126, IEEE. https://doi.org/10.1109/AI4I.2018.8665692
Jena, S. P., Ghosh, S. K., & Tripathy, B. K. (2001). On the theory of bags and lists. Information sciences, 132(1-4), 241-254. https://doi.org/10.1016/S0020-0255(01)00066-4
Jena, S. P., Ghosh, S. K., & Tipathy, B. K. (2002). On the theory of fuzzy bags and fuzzy lists. JOURNAL OF FUZZY MATHEMATICS, 10(1), 85-96.
Kowalski, S., Pavlovska, K. and Goldstein, M., 2009, July. Two case studies in using chatbots for security training. In IFIP World Conference on Information Security Education (pp. 265-272). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39377-8_31
Kumar, P., Sharma, M., Rawat, S. & Choudhury, T. (2018, November). Designing and Developing a Chatbot Using Machine Learning. In 2018 International Conference on System Modeling & Advancement in Research Trends (SMART), pp. 87-91, IEEE. https://doi.org/10.1109/SYSMART.2018.8746972
Nuruzzaman, M. & Hussain, O.K. (2018, October). A Survey on Chatbot Implementation in Customer Service Industry through Deep Neural Networks. In 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), pp. 54-61, IEEE. https://doi.org/10.1109/ICEBE.2018.00019
Orin, T.D. (2017). Implementation of a Bangla chatbot (Doctoral dissertation, BRAC University).Li, X. and Liu, H., 2018. Greedy optimization for K-means-based consensus clustering. Tsinghua Science and Technology, 23(2), pp.184-194. https://doi.org/10.26599/TST.2018.9010063
Pilato, G., Vassallo, G., Augello, A., Vasile, M., & Gaglio, S. (2005). Expert chat-bots for cultural heritage. Intelligenza Artificiale, 2(2), 25-31.
Raghuveer, V., & Tripathy, B. K. (2012). An object oriented approach to improve the precision of learning object retrieval in a self learning environment. Interdisciplinary Journal of E-Learning and Learning Objects, 8(1), 193-214. https://doi.org/10.28945/1740
Raghuveer, V. R., & Tripathy, B. K. (2014, December). Multi dimensional analysis of learning experiences over the e-learning environment for effective retrieval of LOs. In 2014 IEEE Sixth International Conference on Technology for Education, pp. 168-171, IEEE. https://doi.org/10.1109/T4E.2014.7
Raghuveer, V. R., Tripathy, B. K., Singh, T., & Khanna, S. (2014, December). Reinforcement learning approach towards effective content recommendation in MOOC environments. In 2014 IEEE International Conference on MOOC, Innovation and Technology in Education (MITE), pp. 285-289, IEEE. https://doi.org/10.1109/MITE.2014.7020289
Raghuveer, R., & Tripathy, B. K. (2015). On demand analysis of learning experiences for adaptive content retrieval in an e-learning environment. Journal of e-Learning and Knowledge Society, 11(1).
Raghuveer, V. R., & Tripathy, B. K. (2016). Affinity-based learning object retrieval in an e-learning environment using evolutionary learner profile. Knowledge Management & E-Learning: An International Journal, 8(1), 182-199. https://doi.org/10.34105/j.kmel.2016.08.012
Satu, M. S., Akhund, T. M. N. U., & Yousuf, M. A. (2017). Online Shopping Management System with Customer Multi-Language Supported Query handling AIML Chatbot. Institute of Information Technology, Jahangirnagar University. DOI: 10.13140/RG.2.2.10508.10885
Setiaji, B. & Wibowo, F.W. (2016, January). Chatbot using a knowledge in database: human-to-machine conversation modeling. In 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 72-77), IEEE. https://doi.org/10.1109/ISMS.2016.53
How to Cite
Copyright (c) 2020 Srividya, B. K. Tripathy, Neha, Aditi
This work is licensed under a Creative Commons Attribution 4.0 International License.