Abstract
Live-streaming commerce refers to a form of e-commerce that uses live streaming as a channel to achieve marketing objectives, a product of the bidirectional integration of live streaming and commerce in the digital era. However, utilizing large language models for sales in live-streaming commerce faces the challenge of low interaction quality. In this paper, we introduce ChatLsc, a Vtuber designed for live-streaming commerce. Drawing inspiration from leadership behaviors in human societies, we assign different identities and capabilities to agents and encourage their collaboration. In the preparation phase, leaders plan and prepare for the upcoming broadcast based on initial information received from humans. During the live streaming phase, ChatLsc perceives audience comments in real-time through the collaboration of observers, creators, broadcasters, and leaders, combining these with past live streaming activities to generate visual and auditory feedback for the audience. We demonstrate how to use LLMs to interact with audiences in real-time live-streaming commerce, revealing the potential of LLMs for new commercial applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Balaji, P.G., Srinivasan, D.: An introduction to multi-agent systems. In: Srinivasan, D., Jain, L.C. (eds.) Innovations in Multi-agent Systems and Applications-1, vol. 310, pp. 1–27. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14435-6_1
Chen, C.C., Lin, Y.C.: What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics Inform. 35(1), 293–303 (2018)
China Internet Network Information Center: The 52th China statistical report on internet development. Report, China Internet Network Information Center (CINIC) (2023). https://www.cnnic.cn/NMediaFile/2023/0908/MAIN1694151810549M3LV0UWOAV.pdf
Gravitas, S.: Auto-GPT: an autonomous GPT-4 experiment (2023)
Haimson, O.L., Tang, J.C.: What makes live events engaging on Facebook live, periscope, and snapchat. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 48–60 (2017)
Hao, R., Hu, L., Qi, W., Wu, Q., Zhang, Y., Nie, L.: ChatLLM network: more brains, more intelligence. arXiv preprint arXiv:2304.12998 (2023)
Hassan, M.M., Knipper, A., Santu, S.K.K.: ChatGPT as your personal data scientist. arXiv preprint arXiv:2305.13657 (2023)
Irving, G., Christiano, P., Amodei, D.: AI safety via debate. arXiv preprint arXiv:1805.00899 (2018)
Jiang, G., Xu, M., Zhu, S.C., Han, W., Zhang, C., Zhu, Y.: MPI: evaluating and inducing personality in pre-trained language models. arXiv preprint arXiv:2206.07550 (2022)
Kosinski, M.: Theory of mind may have spontaneously emerged in large language models. arXiv preprint arXiv:2302.02083 (2023)
Lewis, M., Yarats, D., Dauphin, Y.N., Parikh, D., Batra, D.: Deal or no deal? End-to-end learning for negotiation dialogues. arXiv preprint arXiv:1706.05125 (2017)
Li, G., Hammoud, H.A.A.K., Itani, H., Khizbullin, D., Ghanem, B.: CAMEL: communicative agents for “mind” exploration of large scale language model society. arXiv preprint arXiv:2303.17760 (2023)
Liang, T., et al.: Encouraging divergent thinking in large language models through multi-agent debate. arXiv preprint arXiv:2305.19118 (2023)
Lin, B.Y., et al.: SwiftSage: a generative agent with fast and slow thinking for complex interactive tasks. arXiv preprint arXiv:2305.17390 (2023)
Mandi, Z., Jain, S., Song, S.: RoCo: dialectic multi-robot collaboration with large language models. arXiv preprint arXiv:2307.04738 (2023)
Radford, A., Jozefowicz, R., Sutskever, I.: Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 (2017)
Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366 (2023)
Sjöblom, M., Hamari, J.: Why do people watch others play video games? An empirical study on the motivations of twitch users. Comput. Hum. Behav. 75, 985–996 (2017)
Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824–24837 (2022)
Wohn, D.Y., Freeman, G., McLaughlin, C.: Explaining viewers’ emotional, instrumental, and financial support provision for live streamers. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2018)
Wongkitrungrueng, A., Assarut, N.: The role of live streaming in building consumer trust and engagement with social commerce sellers. J. Bus. Res. 117, 543–556 (2020)
Xue, J., Liang, X., Xie, T., Wang, H.: See now, act now: how to interact with customers to enhance social commerce engagement? Inf. Manag. 57(6), 103324 (2020)
Yang, Y., Wang, J.: An overview of multi-agent reinforcement learning from game theoretical perspective. arXiv preprint arXiv:2011.00583 (2020)
Zhao, K., Hu, Y., Hong, Y., Westland, J.C.: Understanding characteristics of popular streamers on live streaming platforms: evidence from twitch. tv. J. Assoc. Inf. Syst. (2019, Forthcoming)
Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: MiniGPT-4: enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dai, C., Fang, K., Hua, P., Chan, W.K.(. (2024). ChatLsc: Agents for Live Streaming Commerce. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2024. Lecture Notes in Computer Science(), vol 14735. Springer, Cham. https://doi.org/10.1007/978-3-031-60611-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-60611-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60613-7
Online ISBN: 978-3-031-60611-3
eBook Packages: Computer ScienceComputer Science (R0)