HQA Bot: Hybrid AI Recommender Based Question Answering Chatbot
Keywords:
Artificial Intelligence, Chatbot, Deep Learning, Question Answering System, Recommender System, Reinforcement LearningAbstract
The COVID pandemic has presented a number of challenges for education, particularly when it comes to reaching and engaging students. As a result, online education has become increasingly important, and artificial intelligence (AI) has played a crucial role in supporting this shift. The proposed tutor assistance question-answering system uses AI to automatically generate responses to student questions. This system includes a feedback mechanism, known as a satisfaction index that measures the efficiency of the generated responses and suggest relevant follow-up questions. The proposed Hybrid Recommender-based Dijkstra’s algorithm (HRD) improves the system's accuracy. This algorithm uses a combination of techniques to group relevant questions based on context, which improves the accuracy of identifying the next relevant question. In our customized dataset, this approach achieved an accuracy of 96% and an average accuracy of 82% across benchmarked datasets. With this system, we aim to bridge the gap between students and education by providing a more engaging and personalized learning experience.
Downloads
References
Varghese E, & Pillai M R, 2018, April, A standalone generative conversational interface using deep learning, In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1915-1920), IEEE
Waltz D.L, 1988, The prospects for building truly intelligent machines,Daedalus, 191-212.
Bengio Yoshua, Ducharme Rejean, Vincent Pascal & Janvin Christian, 2003, A neural probabilistic language model, The Journal of Machine Learning Research, vol. 3,pp: 11371155.
Morin Frederic & Bengio, Yoshua, 2005, Hierarchical probabilistic neural network language model, AISTATS, Vol. 5, pp: 246252.
Mnih Andriy & Hinton Geoffrey, 2007, Three new graphical models for statistical language modelling, Proceedings of the 24th International Conference on Machine learning, pp: 641648.
Mikolov Tomas, Chen Kai, Corrado Greg & Dean Jeffrey, 2013, Efficient estimation of word representations in vector space, ArXiv Preprint, ArXiv:1301.3781
Pichponreay, L., Kim, J. H., Choi, C. H., Lee, K. H., & Cho, W. S. (2016, July). Smart answering chatbot based on OCR and overgenerating transformations and ranking. In Ubiquitous and Future Networks (ICUFN), 2016 Eighth International Conference on (pp.1002-1005). IEEE
Conneau Alexis, Kiela Douwe, Schwenk Holger, Barrault Loic & Bordes Antoine, 2017, Supervised learning of universal sentence representations from natural language inference data, ArXiv Preprint, ArXiv:1705.02364.
Chen S,Wen J & Zhang R, 2016, GRU-RNN based question answering over Knowledge Base, In Springer China Conference on Knowledge Graph and Semantic Computing, pp. 8091.
Tan C,Wei F, Zhou Q, Yang N, Du B, Lv W & Zhou M, 2018, Contextaware answer sentence selection with hierarchical gated recurrent neural networks, IEEE/ACM Transaction Audio, Speech, Language Process, Vol. 26, no. 3, pp. 540549.
Seo Minjoon, Kembhavi Aniruddha, Farhadi Ali & Hajishirzi Hannaneh, 2016, Bidirectional attention flow for machine comprehension, arXiv preprint, arXiv:1611.01603.
Zhang Lei & Ma Longxuan, 2017, Coattention based BiLSTM for answer selection, IEEE International Conference on Information and Automation (ICIA), pp. 10051011.
Tan Ming, Dos Santos Cicero, Xiang Bing & Zhou Bowen, 2016, Improved representation learning for question answer matching, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 464473.
Xiang Yang, Chen Qingcai, Wang Xiaolong & Qin Yang, 2017, Answer selection in community question answering via attentive neural networks, IEEE Signal Processing Letters, Vol.24, no.4, pp.505509.
Mueller Jonas & Thyagarajan Aditya, 2016, Siamese recurrent architectures for learning sentence similarity, Proceedings of the AAAI Conference on Artificial Intelligence,Vol.30,no.1.
Zeng Chunqiu, Wang Qing, Mokhtari Shekoofeh & Li Tao, 2016, Online context-aware recommendation with time varying multi-armed bandit, Proceedings of the 22nd ACMSIGKDD international conference on Knowledge discovery and data mining, pp. 20252034.
Watkins Christopher JCH & Dayan Peter, 1992, Q-learning, Machine learning, Springer, Vol. 8, no. 3-4, pp. 279292.
Ansari Mohammad Hossein, Moradi Mohammad, NikRah Omid & Kambakhsh Keyvan M, 2019, CodERS: A hybrid recommender system for an E-learning system, 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS),
Hermann Karl Moritz, Kocisky Tomas, Grefenstette Edward, Espeholt Lasse, Kay Will, Suleyman Mustafa & Blunsom Phil, 2015, Teaching machines to read and comprehend, Advances in Neural Information Processing Systems, pp. 16931701.
Lilian J F, Sundarakantham K, & Shalinie S M, 2021, QeCSO: Design of hybrid Cuckoo Search based Query expansion model for efficient information retrieval, Sdhan, 46(3), 1-11
Patil, A. ., & Govindaraj, S. K. . (2023). Enhanced Deep Learning Models for Efficient Stroke Detection Using MRI Brain Imagery. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 191–198. https://doi.org/10.17762/ijritcc.v11i3.6335
Yulia Sokolova, Deep Learning for Emotion Recognition in Human-Computer Interaction , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
