Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3626772.3661371acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Question Suggestion for Conversational Shopping Assistants Using Product Metadata

Published: 11 July 2024 Publication History

Abstract

Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI). However, customers are often unsure or unaware of how to effectively converse with these assistants to meet their shopping needs. In this work, we emphasize the importance of providing customers a fast, easy to use, and natural way to interact with conversational shopping assistants. We propose a framework that employs Large Language Models (LLMs) to automatically generate contextual, useful, answerable, fluent and diverse questions about products, via in-context learning and supervised fine-tuning. Recommending these questions to customers as helpful suggestions or hints to both start and continue a conversation can result in a smoother and faster shopping experience with reduced conversation overhead and friction. We perform extensive offline evaluations, and discuss in detail about potential customer impact, and the type, length and latency of our generated product questions if incorporated into a real-world shopping assistant.

References

[1]
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
[2]
Janarthanan Balakrishnan and Yogesh K Dwivedi. 2021. Conversational commerce: entering the next stage of AI-powered digital assistants. Annals of Operations Research (2021), 1--35.
[3]
Ankita Bihani, Jeffrey D Ullman, and Andreas Paepcke. 2018. FAQtor: Automatic FAQ generation using online forums. In International Conference on Educational Data Mining. 529--532.
[4]
Yang Deng, Wenxuan Zhang, Qian Yu, and Wai Lam. 2023. Product Question Answering in E-Commerce: A Survey. arXiv preprint arXiv:2302.08092 (2023).
[5]
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023).
[6]
Yingqiang Ge, Wenyue Hua, Kai Mei, Juntao Tan, Shuyuan Xu, Zelong Li, Yongfeng Zhang, et al. 2024. Openagi: When llm meets domain experts. Advances in Neural Information Processing Systems, Vol. 36 (2024).
[7]
Bilal Ghanem, Lauren Lutz Coleman, Julia Rivard Dexter, Spencer von der Ohe, and Alona Fyshe. 2022. Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask. In Findings of the Association for Computational Linguistics: ACL 2022, Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, Ireland, 2131--2146. https://doi.org/10.18653/v1/2022.findings-acl.168
[8]
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, Vol. 35 (2022), 22199--22213.
[9]
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al. 2023. The flan collection: Designing data and methods for effective instruction tuning. arXiv preprint arXiv:2301.13688 (2023).
[10]
Yosi Mass, Boaz Carmeli, Haggai Roitman, and David Konopnicki. 2020. Unsupervised FAQ retrieval with question generation and BERT. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 807--812.
[11]
Nikahat Mulla and Prachi Gharpure. 2023. Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications. Progress in Artificial Intelligence, Vol. 12, 1 (2023), 1--32.
[12]
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). 188--197.
[13]
Vipula Rawte, Amit Sheth, and Amitava Das. 2023. A survey of hallucination in large foundation models. arXiv preprint arXiv:2309.05922 (2023).
[14]
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022a. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022).
[15]
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022b. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, Vol. 35 (2022), 24824--24837.
[16]
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. 2023. Efficient streaming language models with attention sinks. arXiv preprint arXiv:2309.17453 (2023).
[17]
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. 2023 a. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792 (2023).
[18]
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023 b. Siren's song in the AI ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 (2023).
[19]
Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang. 2024. Toolqa: A dataset for llm question answering with external tools. Advances in Neural Information Processing Systems, Vol. 36 (2024).

Cited By

View all
  • (2024)Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-CommerceACM SIGIR Forum10.1145/3687273.368729358:1(1-10)Online publication date: 7-Aug-2024
  • (2024)Research on Tibetan Tourism Viewpoints Information Generation System Based on LLM2024 12th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)10.1109/ICWOC62055.2024.10684948(35-41)Online publication date: 21-Jun-2024

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. conversational shopping assistants
  2. product question suggestion

Qualifiers

  • Short-paper

Conference

SIGIR 2024
Sponsor:

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)90
  • Downloads (Last 6 weeks)10
Reflects downloads up to 24 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-CommerceACM SIGIR Forum10.1145/3687273.368729358:1(1-10)Online publication date: 7-Aug-2024
  • (2024)Research on Tibetan Tourism Viewpoints Information Generation System Based on LLM2024 12th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)10.1109/ICWOC62055.2024.10684948(35-41)Online publication date: 21-Jun-2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media