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Language Model-Driven Chatbot for Business to Address Marketing and Selection of Products

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Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation (TDIT 2020)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 617))

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Abstract

Artificial Intelligence has been increasingly gaining acceptance across advanced functions in numerous fields and industries. This includes marketing, customer support, and leads generation in healthcare, transportation, education, and off late in e-commerce. Machine learning as a subset of artificial intelligence techniques provides various algorithms that enable machines to learn from historical data and make realtime predictions on numbers and texts. Most of the businesses nowadays are trying to increase their reach and making sure that they are available to cater to the customers when they need help. This also enables the companies to market and respond to the queries of potential customers on a realtime basis. Chatter robots or chatbot is one such application of machine learning which allows the business to provide round the clock support to customers and potential leads for marketing questions. Most of the business fail to venture in the domain of hosting chatbot on the website as they do not have enough conversational data with them to train the machine learning algorithm and wait for years to collect enough sample. With the proposed language model-driven chatbots, businesses starting fresh in the domain of the hosting this application can use the user-generated content on social media to fuel the backend framework for the chatbots and start hosting the application.

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References

  1. Balasubraman, S., Peterson, R.A., Jarvenpaa, S.L.: Exploring the implications of m-commerce for markets and marketing. J. Acad. Mark. Sci. 30(4), 348–361 (2002). https://doi.org/10.1177/009207002236910

    Article  Google Scholar 

  2. (Ivy) Yuan, L., Dennis, A.R.: Acting like humans? anthropomorphism and consumer’s willingness to pay in electronic commerce. J. Manage. Inf. Syst. 36(2), 450–477 (2019). https://doi.org/10.1080/07421222.2019.1598691

    Article  Google Scholar 

  3. Lee, G., Lin, H.: Customer perceptions of e-service quality in online shopping. Int. J. Retail Distrib. Manage. 33(2), 161–176 (2005). https://doi.org/10.1108/09590550510581485

    Article  Google Scholar 

  4. Kar, A.K.: Integrating websites with social media – an approach for group decision support. J. Decis. Syst. 24(3), 339–353 (2015). https://doi.org/10.1080/12460125.2015.969585

    Article  Google Scholar 

  5. Pachamanova, D., Lo, V.S.Y., Gülpınar, N.: Uncertainty representation and risk management for direct segmented marketing. J. Mark. Manage. 36(1–2), 149–175 (2020). https://doi.org/10.1080/0267257X.2019.1707265

    Article  Google Scholar 

  6. Koponen, J.P., Rytsy, S.: Social presence and e-commerce B2B chat functions. Eur. J. Mark. 54(6), 1205–1224 (2020). https://doi.org/10.1108/EJM-01-2019-0061

    Article  Google Scholar 

  7. How Industry Will be Affected by Tech in the Future: Business Fundas. https://www.business-fundas.com/2019/how-industry-will-be-affected-by-techin-the-future/

  8. 5 Great Ways Big Data can Help Small Businesses Thrive: Tech Talk 29 October 2019. https://tech-talk.org/2019/10/29/5-great-ways-big-data-can-help-smallbusinesses-thrive/

  9. Alba, J.W., Hutchinson, J.W.: Dimensions of consumer expertise. J. Consum. Res. 13(4), 411–454 (1987). https://doi.org/10.1086/209080

    Article  Google Scholar 

  10. Applications of Machine Learning in Business – Business Frontiers: https://business-frontiers.org/2020/07/24/applications-of-machine-learning-in-business/

  11. Rathore, A.K., Kar, A.K., Ilavarasan, P.V.: Social media analytics: literature review and directions for future research. Decis. Anal. 14(4), 229–249 (2017). https://doi.org/10.1287/deca.2017.0355

    Article  MathSciNet  Google Scholar 

  12. Rai, A.: Editor’s comments: diversity of design science research. MIS Q. 41(1), iii–xviii (2017)

    Google Scholar 

  13. Pries-Heje, J., Baskerville, R.: The design theory nexus. MIS Q. 32(4), 731–755 (2008). https://doi.org/10.2307/25148870

    Article  Google Scholar 

  14. Khalifa, M., Liu, V.: Satisfaction with internet-based services: the role of expectations and desires. Int. J. Electron. Commer. 7(2), 31–49 (2002). https://doi.org/10.1080/10864415.2002.11044267

    Article  Google Scholar 

  15. Kaynama, S.A., Christine, I.: A proposal to assess the service quality of online travel agencies: an exploratory study. J. Prof. Serv. Mark. 21(1), 63–88 (2000). https://doi.org/10.1300/j090v21n01_05

    Article  Google Scholar 

  16. Loiacono, E.T., Watson, R.T., Goodhue, D.L.: WEBQUAL: a measure of website quality. In: American Marketing Association. Conference Proceedings, 13, pp. 432–438, p. 71 (2002)

    Google Scholar 

  17. Shchiglik, C., Barnes, S.J.: Evaluating website quality in the airline industry. J. Comput. Inf. Syst. 44(3), 17–25 (2004). https://doi.org/10.1080/08874417.2004.11647578

    Article  Google Scholar 

  18. Cases, A.-S.: Perceived risk and risk-reduction strategies in Internet shopping. Int. Rev. Retail Distrib. Consum. Res. 12(4), 375–394 (2002). https://doi.org/10.1080/09593960210151162

    Article  Google Scholar 

  19. Cheung, C.M.K., Chan, G.W.W., Limayem, M.: A critical review of online consumer behaviour: empirical research. J. Electron. Commer. Organ. 3, 1–19 (2005)

    Article  Google Scholar 

  20. Childers, T.L., Carr, C.L., Peck, J., Carson, S.: Hedonic and utilitarian motivations for online retail shopping behavior. J. Retail. 77(4), 511–535 (2001). https://doi.org/10.1016/S0022-4359(01)00056-2

    Article  Google Scholar 

  21. Johnson, E.J., Moe, W.W., Fader, P.S., Bellman, S., Lohse, G.L.: On the depth and dynamics of online search behavior. Manage. Sci. 50(3), 299–308 (2004). https://doi.org/10.1287/mnsc.1040.0194

    Article  Google Scholar 

  22. Khalifa, M., Liu, V.: Online consumer retention: contingent effects of online shopping habit and online shopping experience. Eur. J. Inf. Syst. 16(6), 780–792 (2007). https://doi.org/10.1057/palgrave.ejis.3000711

    Article  Google Scholar 

  23. Jeong, S.-S., Seo, Y.-S.: Improving response capability of chatbot using twitter. J. Ambient Intell. Hum. Comput. (2019). https://doi.org/10.1007/s12652-019-01347-6

  24. D’silva, G.M., Thakare, S., More, S., Kuriakose, J.: Real world smart chatbot for customer care using a software as a service (SaaS) architecture. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 658–664 Febraury 2017. https://doi.org/10.1109/i-smac.2017.8058261

  25. Mauldin,M.L.: Chatterbots, tinymuds, and the turing test entering the loebner prize competition. In: Proceedings of the Twelfth AAAI National Conference on Artificial Intelligence, Seattle, Washington, pp. 16–21 August 1994

    Google Scholar 

  26. Baby, C.J., Khan, F.A., Swathi, J.N.: Home automation using IoT and a chatbot using natural language processing. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1–6 April 2017. https://doi.org/10.1109/ipact.2017.8245185

  27. Rajkumar, R., Ganapathy, V.: Bio-inspiring learning style chatbot inventory using brain computing interface to increase the efficiency of E-learning. IEEE Access 8, 67377–67395 (2020). https://doi.org/10.1109/ACCESS.2020.2984591

    Article  Google Scholar 

  28. Cerezo, J., Kubelka, J., Robbes, R., Bergel, A.: Building an Expert Recommender Chatbot. In: 2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE), pp. 59–63 May 2019. https://doi.org/10.1109/botse.2019.00022

  29. Le, Q., Mikolov, T.: Distributed Representations of Sentences and Documents. pp. 9. ICML (2014)

    Google Scholar 

  30. Chakraborty, A., Kar, A.K.: Swarm intelligence: a review of algorithms. In: Patnaik, S., Yang, X.-S., Nakamatsu, K. (eds.) Nature-Inspired Computing and Optimization. MOST, vol. 10, pp. 475–494. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50920-4_19

    Chapter  Google Scholar 

  31. Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, pp. 1631–1642 October 2013

    Google Scholar 

  32. Zhao, H., Lu, Z., Poupart, P.: Self-adaptive hierarchical sentence model. In: Presented at the Twenty-Fourth International Joint Conference on Artificial Intelligence Jun 2015

    Google Scholar 

  33. Kushwaha, A.K., Kar, A.K., Vigneswara Ilavarasan, P.: Predicting information diffusion on twitter a deep learning neural network model using custom weighted word features. In: Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y.K., Mäntymäki, M. (eds.) I3E 2020. LNCS, vol. 12066, pp. 456–468. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44999-5_38

    Chapter  Google Scholar 

  34. Hassanpour, S., Tomita, N., DeLise, T., Crosier, B., Marsch, L.A.: Identifying substance use risk based on deep neural networks and Instagram social media data. Nature 44(3), 487–494 (2019). https://doi.org/10.1038/s41386-018-0247x. Art. no. 3

    Article  Google Scholar 

  35. Quiroz, J.C., Laranjo, L., Kocaballi, A.B., Berkovsky, S., Rezazadegan, D., Coiera, E.: Challenges of developing a digital scribe to reduce clinical documentation burden. Nature 2(1), 114 (2019). https://doi.org/10.1038/s41746019-0190-1. Art. no. 1

    Article  Google Scholar 

  36. Reich, T., Maglio, S.J.: Featuring mistakes: the persuasive impact of purchase mistakes in online reviews. J. Mark. 84(1), 52–65 (2020). https://doi.org/10.1177/0022242919882428

    Article  Google Scholar 

  37. Netzer, O., Feldman, R., Goldenberg, J., Fresko, M.: Mine your own business: market-structure surveillance through text mining. Mark. Sci. 31(3), 521–543 (2012). https://doi.org/10.1287/mksc.1120.0713

    Article  Google Scholar 

  38. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5–8, 2013, Lake Tahoe, Nevada, United States, pp 3111–3119 (2013). arXiv:1301.3781 [cs], September 2013

  39. Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013). arXiv:1309.4168 [cs] September 2013

  40. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ICLR (2015). arXiv:1412.6980 [cs] January 2017

  41. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781 [cs], September 2013, Accessed: 26 Jul. 2020. [Online]. Available: http://arxiv.org/abs/1301.3781

  42. Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Bernardi, R., Zamparelli, R.: A SICK cure for the evaluation of compositional distributional semantic models. pp. 9. ICML

    Google Scholar 

  43. Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. ACL (2015). arXiv:1503.00075 [cs]

  44. Kar, A.K., Rakshit, A.: Flexible pricing models for cloud computing based on group decision making under consensus. Global J. Flex. Syst. Manage. 16(2), 191–204 (2015). https://doi.org/10.1007/s40171-015-0093-1

    Article  Google Scholar 

  45. Aswani, R., Ghrera, S.P., Kar, A.K., Chandra, S.: Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc. Netw. Anal. Min. 7(1), 1–10 (2017). https://doi.org/10.1007/s13278-017-0461-2

    Article  Google Scholar 

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Correspondence to Amit Kumar Kushwaha .

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Kushwaha, A.K., Kar, A.K. (2020). Language Model-Driven Chatbot for Business to Address Marketing and Selection of Products. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-030-64849-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-64849-7_3

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