Notes on paper: Large Language Models as Zero-Shot Conversational Recommenders

Link to paper https://arxiv.org/abs/2308.10053 Notes CRS possesses the potential to: (1) understand not only users’ historical actions but also users’ (multi-turn) natural-language inputs; (2) Provide not only recommended items but also human-like responses for multiple purposes, such as preference refinement, knowledgeable discussion, or recommendation justification.Towards this a typical conversational recommender Read more…

Notes on Paper: RecMind: Large Language Model Powered Agent For Recommendation

Link to paper https://arxiv.org/abs/2308.14296 Notes The paper propose a novel algorithm, Self-Inspiring, to improve the planning ability of the LLM agent. At each intermediate planning step, the LLM “self-inspires” to consider all previously explored states to plan for next step. Literature survey Architecture Tools they used 1) DB tool1) To Read more…