让聊天推荐系统实时跟上电商商品变动，更新快且省资源。
Conversational Recommendation over Live E-Commerce Catalogues with Self-Refreshing Retrieval
- 用哈希比对识别商品增删改，只处理变化部分，避免全量重建。
- 对话中仅用大模型判断意图，检索和排序由专用模块完成。
- 可在微信等平台部署，支持动态商品库的实时推荐，适合电商场景。
基于大语言模型的对话推荐系统通常在静态商品库上评估，但电商平台的商品目录持续变动，包括新品上架、价格调整和补货。本文提出一种无商家依赖的多轮对话购物助手，可运行于动态商品库。核心是自刷新检索器：接收商家商品数据流，丰富记录并同步至向量索引；通过每项商品的哈希值识别新增、修改、删除或未变商品，仅处理差异部分，避免重建整个目录。对话控制层利用该索引，仅用大模型进行意图分类与偏好收集，而检索、重排序与多样性选择由专用函数执行。演示为一个基于 WhatsApp 的实时聊天机器人，商品变动在下次成功同步后即反映到推荐中。项目代码、文档及操作视频见 https://github.com/infobip/infobip-agentic-crs。
原文摘要 · Abstract (English)
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.
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