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Click-to-Ask: An AI Live Streaming Assistant with Offline Copywriting and Online Interactive QA

Ruizhi Yu, Keyang Zhong, Peng Liu, Qi Wu, Haoran Zhang, Yanhao Zhang, Chen Chen, Haonan Lu

Abstract

Live streaming commerce has become a prominent form of broadcasting in the modern era. To facilitate more efficient and convenient product promotions for streamers, we present Click-to-Ask, an AI-driven assistant for live streaming commerce with complementary offline and online components. The offline module processes diverse multimodal product information, transforming complex inputs into structured product data and generating compliant promotional copywriting. During live broadcasts, the online module enables real-time responses to viewer inquiries by allowing streamers to click on questions and leveraging both the structured product information generated by the offline module and an event-level historical memory maintained in a streaming architecture. This system significantly reduces the time needed for promotional preparation, enhances content engagement, and enables prompt interaction with audience inquiries, ultimately improving the effectiveness of live streaming commerce. On our collected dataset of TikTok live stream frames, the proposed method achieves a Question Recognition Accuracy of 0.913 and a Response Quality score of 0.876, demonstrating considerable potential for practical application. The video demonstration can be viewed here: https://www.youtube.com/shorts/mWIXK-SWhiE.

Click-to-Ask: An AI Live Streaming Assistant with Offline Copywriting and Online Interactive QA

Abstract

Live streaming commerce has become a prominent form of broadcasting in the modern era. To facilitate more efficient and convenient product promotions for streamers, we present Click-to-Ask, an AI-driven assistant for live streaming commerce with complementary offline and online components. The offline module processes diverse multimodal product information, transforming complex inputs into structured product data and generating compliant promotional copywriting. During live broadcasts, the online module enables real-time responses to viewer inquiries by allowing streamers to click on questions and leveraging both the structured product information generated by the offline module and an event-level historical memory maintained in a streaming architecture. This system significantly reduces the time needed for promotional preparation, enhances content engagement, and enables prompt interaction with audience inquiries, ultimately improving the effectiveness of live streaming commerce. On our collected dataset of TikTok live stream frames, the proposed method achieves a Question Recognition Accuracy of 0.913 and a Response Quality score of 0.876, demonstrating considerable potential for practical application. The video demonstration can be viewed here: https://www.youtube.com/shorts/mWIXK-SWhiE.
Paper Structure (12 sections, 3 equations, 2 figures, 1 table)

This paper contains 12 sections, 3 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Framework
  3. Offline copywriting module
  4. Product information integration
  5. Copywriting generation
  6. Prohibited-term purification
  7. Online interactive Q&A module
  8. Training Data Synthesis
  9. Click-based streaming response learning
  10. Event-based accelerated segment for history memory storage
  11. Experiments
  12. Conclusion

Figures (2)

  • Figure 1: Framework of Click-to-Ask. The upper half illustrates the offline copywriting module, including product information integration, copywriting generation, prohibited-term purification, and the structured product database. The lower half shows the online interactive Q&A module on the left, responsible for real-time bullet-chat responses, and the event-based history memory module on the right, which asynchronously segments the video stream and accelerates historical caption generation.
  • Figure 2: Some visual results on mobile devices.