Problem
Research questions and friction points this paper is trying to address.
conversational recommender systems
live e-commerce catalogues
self-refreshing retrieval
dynamic product changes
Conversational Recommendation over Live E-Commerce Catalogues with Self-Refreshing Retrieval
🤖 AI Summary
该研究针对电商目录动态变化的问题，提出了一种基于自刷新检索器的多轮对话推荐系统，通过增量处理和大语言模型实现高效推荐。
📝 Abstract
Conversational recommender systems based on large language models (LLMs) are usually evaluated on static, pre-indexed item collections, yet e-commerce catalogues change continuously as products are added or removed, repriced, and restocked. We present a merchant-agnostic, multi-turn conversational shopping assistant that operates over such live catalogues. Its central component is a self-refreshing retriever that ingests a merchant product feed, enriches the records, and synchronizes them into a vector index. On each run, per-item hashes identify which products are new, changed, deleted, or unchanged, so only the delta is processed rather than rebuilding the whole catalogue. A controller-based dialogue layer consumes this index, using an LLM only for intent classification and preference elicitation while retrieval, reranking, and diversity selection run as dedicated functions. Our demonstration is a WhatsApp shopping assistant in which catalogue changes reach the recommendations after the next successful sync. A live chatbot, documentation, and a recorded walkthrough are available at https://github.com/infobip/infobip-agentic-crs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
self-refreshing retriever
live e-commerce catalogues
multi-turn conversational assistant
vector index
🔎 Similar Papers
No similar papers found.
