Summary of Large Language Model Driven Recommendation, by Anton Korikov et al.
Large Language Model Driven Recommendation
by Anton Korikov, Scott Sanner, Yashar Deldjoo, Zhankui He, Julian McAuley, Arnau Ramisa, Rene Vidal, Mahesh Sathiamoorthy, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This chapter explores the potential of large language models (LLMs) for building highly personalized recommendation systems. By leveraging natural language interactions, LLMs can connect nuanced user preferences to items via interactive dialogues. The authors present a taxonomy of data sources for language-driven recommendations, including item descriptions, user-system interactions, and user profiles. They then review fundamental techniques for LLM recommendation, such as encoder-only and autoregressive models in tuned and untuned settings. Additionally, the chapter discusses multi-module architectures that integrate LLMs with components like retrievers and recommendation systems. Finally, it introduces architectures for conversational recommender systems, which facilitate multi-turn dialogues for preference elicitation, critiquing, and question-answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to make personalized recommendations. Normally, we use simple feedback like what you buy or click on to make recommendations. But with these language models, we can ask users questions and get more detailed information about what they want. This helps us connect the right items to the user’s preferences. The authors group different types of data together that we can use for this kind of recommendation, like descriptions of things, how people interact with systems, and profiles of individual users. They also explain some basic ways to use these language models for recommendations, and how we can combine them with other tools to make even better suggestions. Finally, they talk about having conversations with users to get more information and make even more personalized recommendations. |
Keywords
» Artificial intelligence » Autoregressive » Encoder » Question answering
