Multiheaded deep learning chatbot for increasing production and marketing |
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| Institution: | 1. School of Business, Guilin University of Electronic Technology, Guilin 541000, China;3. Faculty of Computing and Multimedia, Universiti Poly-Tech Malaysia, Malaysia;4. Baoji Vocational & Technical College, Baoji 721000, China;5. Institute of Innovation, Science and Sustainability, Federation University Australia, Brisbane, QLD 4000, Australia;1. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal;2. University of Coimbra, CISUC, Department of Informatics Engineering, Coimbra, Portugal;1. School of Information Management, Nanjing University, Nanjing 210023, PR China;2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210093, PR China;3. Science, Mathematics and Technology, Singapore University of Technology and Design, 487372, Singapore;1. College of Economics, Shenzhen University, Shenzhen, Guangdong 518060, China;2. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China;1. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Department of Economics, University of Reading, Reading RG6 6UD, UK;1. Outdoor Recreation & Data Lab, University of Washington, Seattle, WA, USA;2. USDA Forest Service, Pacific Northwest Research Station, Seattle, WA, USA;3. eScience Institute, University of Washington, Seattle, WA, USA. |
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| Abstract: | Some businesses on product development prefer to use a chatbot for judging the customer's view. Today, the ability of a chatbot to consider the context is challenging due to its technical nature. Sometimes, it may misjudge the context, making the wrong decision in predicting the product's originality in the market. This task of chatbot helps the enterprise make huge profits from accurate predictions. However, chatbots may commit errors in dialogs and bring inappropriate responses to users, reducing the confidentiality of product and marketing information. This, in turn, reduces the enterprise gain and imposes cost complications on businesses. To improve the performance of chatbots, AI models are used based on deep learning concepts. This research proposes a multi-headed deep neural network (MH-DNN) model for addressing the logical and fuzzy errors caused by retrieval chatbot models. This model cuts down on the error raised from the information loss. Our experiments extensively trained the model on a large Ubuntu dialog corpus. The recall evaluation scores showed that the MH-DNN approach slightly outperformed selected state-of-the-art retrieval-based chatbot approaches. The results obtained from the MHDNN augmentation approach were pretty impressive. In our proposed work, the MHDNN algorithm exhibited accuracy rates of 94% and 92%, respectively, with and without the help of the Seq2Seq technique. |
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