LLM-Based Recommenders

• A Multi-facet Paradigm to Bridge Large Language Model and Recommendation
  Large Language Models (LLMs) have garnered considerable attention in recommender systems. To achieve LLM-based recommendation, item indexing and generation grounding are two essential steps, bridging …
• All AI Models are Wrong, but Some are Optimal
  Abstract—AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models often resu…
• Can AI Explanations Make You Change Your Mind?
  In the context of AI-based decision support systems, explanations can help users to judge when to trust the AI’s suggestion, and when to question it. In this way, human oversight can prevent AI errors…
• CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
  Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text sem…
• Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews
  “Deciding on a product to purchase can be a time-consuming process. Every user has specific quality preferences, budget restrictions, or enjoys different item features. To distill important informatio…
• Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
  **there is a v3** The investigation thoroughly explores the inherent strengths of LLMs within recommendation frameworks, encompassing nuanced contextual comprehension, seamless transitions across div…
• GenRec: Large Language Model for Generative Recommendation
  “Traditionally, recommendation systems have been built around methods such as collaborative filtering [5, 6, 14], content-based filtering [16, 18], and hybrid approaches [1, 11]. Collaborative filteri…
• Generating Query-Relevant Document Summaries via Reinforcement Learning
  E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predictions, as product ti…
• Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
  While large language models (LLMs) offer superior ranking capabilities, it is challenging to deploy LLMs in real-time systems due to the high-latency requirements. To leverage the ranking power of LLM…
• Large Language Models are Zero-Shot Rankers for Recommender Systems
  “To carry out this study, we first formalize the recommendation process of LLMs as a conditional ranking task. Given prompts that include sequential historical interactions as “conditions”, LLMs are i…
• Learning Pluralistic User Preferences through Reinforcement Learning Fine-tuned Summaries
  As everyday use cases of large language model (LLM) AI assistants have expanded, it is becoming increasingly important to personalize responses to align to different users’ preferences and goals. Whil…
• On Generative Agents in Recommendation
  we envision a recommendation simulator, capitalizing on recent breakthroughs in human-level intelligence exhibited by Large Language Models (LLMs). We propose Agent4Rec, a user simulator in recommenda…
• Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis
  “In order to alleviate the problem of information overload [31, 76], recommender systems explore the needs of users and provide them with recommendations based on their historical interactions, which …
• Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics
  We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses. The system uses Deep Reinforcement Learning (DRL) to ge…
• RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability
  we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three …