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Continuum Knowledge
  • Continuum
  • Data
    • Datasets
      • Pre Training Data
      • Types of Fine Tuning
      • Self Instruct Paper
      • Self-Alignment with Instruction Backtranslation
      • Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets
      • Instruction Tuning
      • Instruction Fine Tuning - Alpagasus
      • Less is More For Alignment
      • Enhanced Supervised Fine Tuning
      • Visualising Data using t-SNE
      • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
      • Training and Evaluation Datasets
      • What is perplexity?
  • MODELS
    • Foundation Models
      • The leaderboard
      • Foundation Models
      • LLama 2 - Analysis
      • Analysis of Llama 3
      • Llama 3.1 series
      • Google Gemini 1.5
      • Platypus: Quick, Cheap, and Powerful Refinement of LLMs
      • Mixtral of Experts
      • Mixture-of-Agents (MoA)
      • Phi 1.5
        • Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach
      • Phi 2.0
      • Phi-3 Technical Report
  • Training
    • The Fine Tuning Process
      • Why fine tune?
        • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
        • Explanations in Fine Tuning
      • Tokenization
        • Tokenization Is More Than Compression
        • Tokenization - SentencePiece
        • Tokenization explore
        • Tokenizer Choice For LLM Training: Negligible or Crucial?
        • Getting the most out of your tokenizer for pre-training and domain adaptation
        • TokenMonster
      • Parameter Efficient Fine Tuning
        • P-Tuning
          • The Power of Scale for Parameter-Efficient Prompt Tuning
        • Prefix-Tuning: Optimizing Continuous Prompts for Generation
        • Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models
        • What is Low-Rank Adaptation (LoRA) - explained by the inventor
        • Low Rank Adaptation (Lora)
        • Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)
        • QLORA: Efficient Finetuning of Quantized LLMs
        • Bits and Bytes
        • The Magic behind Qlora
        • Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation
        • The quantization constant
        • QLORA: Efficient Finetuning of Quantized Language Models
        • QLORA and Fine-Tuning of Quantized Language Models (LMs)
        • ReLoRA: High-Rank Training Through Low-Rank Updates
        • SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
        • GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection
      • Hyperparameters
        • Batch Size
        • Padding Tokens
        • Mixed precision training
        • FP8 Formats for Deep Learning
        • Floating Point Numbers
        • Batch Size and Model loss
        • Batch Normalisation
        • Rethinking Learning Rate Tuning in the Era of Language Models
        • Sample Packing
        • Gradient accumulation
        • A process for choosing the learning rate
        • Learning Rate Scheduler
        • Checkpoints
        • A Survey on Efficient Training of Transformers
        • Sequence Length Warmup
        • Understanding Training vs. Evaluation Data Splits
        • Cross-entropy loss
        • Weight Decay
        • Optimiser
        • Caching
      • Training Processes
        • Extending the context window
        • PyTorch Fully Sharded Data Parallel (FSDP)
        • Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
        • YaRN: Efficient Context Window Extension of Large Language Models
        • Sliding Window Attention
        • LongRoPE
        • Reinforcement Learning
        • An introduction to reinforcement learning
        • Reinforcement Learning from Human Feedback (RLHF)
        • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • INFERENCE
    • Why is inference important?
      • Grouped Query Attention
      • Key Value Cache
      • Flash Attention
      • Flash Attention 2
      • StreamingLLM
      • Paged Attention and vLLM
      • TensorRT-LLM
      • Torchscript
      • NVIDIA L40S GPU
      • Triton Inference Server - Introduction
      • Triton Inference Server
      • FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference
      • Is PUE a useful measure of data centre performance?
      • SLORA
  • KNOWLEDGE
    • Vector Databases
      • A Comprehensive Survey on Vector Databases
      • Vector database management systems: Fundamental concepts, use-cases, and current challenges
      • Using the Output Embedding to Improve Language Models
      • Decoding Sentence-BERT
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • SimCSE: Simple Contrastive Learning of Sentence Embeddings
      • Questions Are All You Need to Train a Dense Passage Retriever
      • Improving Text Embeddings with Large Language Models
      • Massive Text Embedding Benchmark
      • RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
      • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
      • Embedding and Fine-Tuning in Neural Language Models
      • Embedding Model Construction
      • Demystifying Embedding Spaces using Large Language Models
      • Fine-Tuning Llama for Multi-Stage Text Retrieval
      • Large Language Model Based Text Augmentation Enhanced Personality Detection Model
      • One Embedder, Any Task: Instruction-Finetuned Text Embeddings
      • Vector Databases are not the only solution
      • Knowledge Graphs
        • Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration
        • Unifying Large Language Models and Knowledge Graphs: A Roadmap
      • Approximate Nearest Neighbor (ANN)
      • High Dimensional Data
      • Principal Component Analysis (PCA)
      • Vector Similarity Search - HNSW
      • FAISS (Facebook AI Similarity Search)
      • Unsupervised Dense Retrievers
    • Retrieval Augmented Generation
      • Retrieval-Augmented Generation for Large Language Models: A Survey
      • Fine-Tuning or Retrieval?
      • Revolutionising Information Retrieval: The Power of RAG in Language Models
      • A Survey on Retrieval-Augmented Text Generation
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • Retrieve Anything To Augment Large Language Models
      • Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators
      • Active Retrieval Augmented Generation
      • DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints
      • DSPy: Compiling Declarative Language Model Calls
      • DSPy: In-Context Learning for Extreme Multi-Label Classification
      • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
      • HYDE: Revolutionising Search with Hypothetical Document Embeddings
      • Enhancing Recommender Systems with Large Language Model Reasoning Graphs
      • Retrieval Augmented Generation (RAG) versus fine tuning
      • RAFT: Adapting Language Model to Domain Specific RAG
      • Summarisation Methods and RAG
      • Lessons Learned on LLM RAG Solutions
      • Stanford: Retrieval Augmented Language Models
      • Overview of RAG Approaches with Vector Databases
      • Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems
    • Semantic Routing
    • Resource Description Framework (RDF)
  • AGENTS
    • What is agency?
      • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
      • Types of Agents
      • The risk of AI agency
      • Understanding Personality in Large Language Models: A New Frontier in AI Psychology
      • AI Agents - Reasoning, Planning, and Tool Calling
      • Personality and Brand
      • Agent Interaction via APIs
      • Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon
      • A Survey on Language Model based Autonomous Agents
      • Large Language Models as Agents
      • AI Reasoning: A Deep Dive into Chain-of-Thought Prompting
      • Enhancing AI Reasoning with Self-Taught Reasoner (STaR)
      • Exploring the Frontier of AI: The "Tree of Thoughts" Framework
      • Toolformer: Revolutionising Language Models with API Integration - An Analysis
      • TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion
      • Unleashing the Power of LLMs in API Integration: The Rise of Gorilla
      • Andrew Ng's presentation on AI agents
      • Making AI accessible with Andrej Karpathy and Stephanie Zhan
  • Regulation and Ethics
    • Regulation and Ethics
      • Privacy
      • Detecting AI Generated content
      • Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs
      • Adverse Reactions to generative AI
      • Navigating the Ethical Minefield: The Challenge of Security in Large Language Models
      • Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making
  • DISRUPTION
    • Data Architecture
      • What is a data pipeline?
      • What is Reverse ETL?
      • Unstructured Data and Generatve AI
      • Resource Description Framework (RDF)
      • Integrating generative AI with the Semantic Web
    • Search
      • BM25 - Search Engine Ranking Function
      • BERT as a reranking engine
      • BERT and Google
      • Generative Engine Optimisation (GEO)
      • Billion-scale similarity search with GPUs
      • FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
      • Neural Collaborative Filtering
      • Federated Neural Collaborative Filtering
      • Latent Space versus Embedding Space
      • Improving Text Embeddings with Large Language Models
    • Recommendation Engines
      • On Interpretation and Measurement of Soft Attributes for Recommendation
      • A Survey on Large Language Models for Recommendation
      • Model driven recommendation systems
      • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
      • Foundation Models for Recommender Systems
      • Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
      • AI driven recommendations - harming autonomy?
    • Logging
      • A Taxonomy of Anomalies in Log Data
      • Deeplog
      • LogBERT: Log Anomaly Detection via BERT
      • Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
      • Log-based Anomaly Detection with Deep Learning: How Far Are We?
      • Deep Learning for Anomaly Detection in Log Data: A Survey
      • LogGPT
      • Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis
  • Infrastructure
    • The modern data centre
      • Enhancing Data Centre Efficiency: Strategies to Improve PUE
      • TCO of NVIDIA GPUs and falling barriers to entry
      • Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator
      • Data Centres
      • Liquid Cooling
    • Servers and Chips
      • The NVIDIA H100 GPU
      • NVIDIA H100 NVL
      • Lambda Hyperplane 8-H100
      • NVIDIA DGX Servers
      • NVIDIA DGX-2
      • NVIDIA DGX H-100 System
      • NVLink Switch
      • Tensor Cores
      • NVIDIA Grace Hopper Superchip
      • NVIDIA Grace CPU Superchip
      • NVIDIA GB200 NVL72
      • Hopper versus Blackwell
      • HGX: High-Performance GPU Platforms
      • ARM Chips
      • ARM versus x86
      • RISC versus CISC
      • Introduction to RISC-V
    • Networking and Connectivity
      • Infiniband versus Ethernet
      • NVIDIA Quantum InfiniBand
      • PCIe (Peripheral Component Interconnect Express)
      • NVIDIA ConnectX InfiniBand adapters
      • NVMe (Non-Volatile Memory Express)
      • NVMe over Fabrics (NVMe-oF)
      • NVIDIA Spectrum-X
      • NVIDIA GPUDirect
      • Evaluating Modern GPU Interconnect
      • Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)
      • Next-generation networking in AI environments
      • NVIDIA Collective Communications Library (NCCL)
    • Data and Memory
      • NVIDIA BlueField Data Processing Units (DPUs)
      • Remote Direct Memory Access (RDMA)
      • High Bandwidth Memory (HBM3)
      • Flash Memory
      • Model Requirements
      • Calculating GPU memory for serving LLMs
      • Transformer training costs
      • GPU Performance Optimisation
    • Libraries and Complements
      • NVIDIA Base Command
      • NVIDIA AI Enterprise
      • CUDA - NVIDIA GTC 2024 presentation
      • RAPIDs
      • RAFT
    • Vast Data Platform
      • Vast Datastore
      • Vast Database
      • Vast Data Engine
      • DASE (Disaggregated and Shared Everything)
      • Dremio and VAST Data
    • Storage
      • WEKA: A High-Performance Storage Solution for AI Workloads
      • Introduction to NVIDIA GPUDirect Storage (GDS)
        • GDS cuFile API
      • NVIDIA Magnum IO GPUDirect Storage (GDS)
      • Vectors in Memory
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Copyright Continuum Labs - 2023

On this page
  • Key Points Covered in the Paper
  • LLM-Powered Recommender Systems Across Domains
  • LLMs in Sequential Recommender Systems
  • Off-the-shelf LLM-based recommendation system
  • Personalised Recommenders Systems
  • Fine-tuned LLMs used in recommender systems
  • Contributions of the Paper

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  1. DISRUPTION
  2. Recommendation Engines

Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review

PreviousFoundation Models for Recommender SystemsNextAI driven recommendations - harming autonomy?

Last updated 8 months ago

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This March 2024 paper discusses the role of Large Language Models (LLMs) in enhancing recommender systems.

Authored by Arpita Vats and Rahul Raja, the study focuses on how LLMs leverage their language comprehension and generation capabilities to improve recommendation processes traditionally limited by the lack of direct user interaction data.

Key Points Covered in the Paper

  • Integration of LLMs in Recommender Systems: The paper details how LLMs are integrated into recommender systems, making them capable of understanding context, user preferences, and subtleties in user requests without the need for explicit behavioural data.

  • Advantages of LLMs: LLMs offer a nuanced understanding of language, which allows for more accurate and contextually aware item recommendations. This represents a shift from traditional systems that rely heavily on user-item interaction matrices and explicit feedback.

  • Systematic Taxonomy and Techniques: The authors provide a systematic taxonomy of LLM applications in recommender systems and detail the primary techniques that illustrate how these models are currently used.

  • Challenges and Solutions: The paper also discusses the challenges associated with implementing LLMs in recommender systems, such as sensitivity to input prompts and potential for misinterpretations. It offers insights into ongoing research aimed at addressing these issues.

LLM-Powered Recommender Systems Across Domains

LlamaRec: Uses a two-stage approach where the first stage employs a sequential recommender to filter candidate items based on user history. The second stage uses a tailored prompt with an LLM to predict user preferences more accurately, using a verbaliser for efficient probability distribution.

RecMind: Operates as an autonomous agent using a Self-Inspiring (SI) planning algorithm, which enables it to generate personalised recommendations by considering previous interactions and external tools, enhancing decision-making and memory processes.

RecRec: Aims to provide algorithmic recourse by suggesting actionable changes users can make to influence the recommendation outcomes. It focuses on creating transparent recommendations, validated through empirical testing.

P5: Implements a unified framework called "Pretrain, Personalised Prompt & Predict Paradigm" for various recommendation tasks. It uses personalised prompts to adapt recommendations dynamically, showcasing strong zero-shot capabilities in new domains.

RecExplainer: Offers a method to enhance transparency in recommendation systems by aligning LLM outputs with recommendation models through behavior and intention alignment methods, facilitating high-quality explanations.

DOKE: Integrates domain-specific knowledge into LLMs without extensive training by using an external domain knowledge extractor, enhancing the relevance of recommendations.

RLMRec: A model-agnostic framework that leverages LLMs for enhancing recommendations in graph-based systems, particularly effective in noisy environments or with incomplete data.

RARS: Combines retrieval-based and generation-based models to improve recommendations, especially useful in scenarios with sparse data.

GenRec: Uses descriptive item information to create engaging and personalised recommendations, demonstrating effectiveness in diverse applications.

Recommender AI Agent (RecAgent): Merges LLMs with traditional recommender models to form an interactive conversational system, leveraging the strengths of both for enhanced user interaction and recommendation accuracy.

CoLLM: Focuses on integrating collaborative information with text semantics to improve recommendations in both cold and warm start scenarios.

POSO: Enhances pre-existing recommendation modules with specialized sub-modules tailored for different user groups, significantly improving performance with minimal overhead.

LLMs in Sequential Recommender Systems

PDRec employs diffusion models to analyse user preferences dynamically, adapting to data sparsity by integrating time-interval diffusion, enhancing positive instance potential, and sampling from diffusion outputs.

G-Meta is optimised for GPU clusters, enhancing distributed training efficiency through data and model parallelism, significantly reducing model delivery time in Alibaba’s systems.

ELCRec uses a representation learning and clustering optimization framework to capture user intents, enhancing recommendation performance by learning cluster centres for scalability.

GPTRec uses a GPT-2-based model with SVD Tokenisation for sequential recommendation, reducing embedding table size while maintaining quality on datasets like MovieLens-1M.

DRDT introduces dynamic reflection with divergent thinking within a retriever-reranker framework to analyze user preferences comprehensively.

LLaRA combines traditional ID-based embeddings with LLM textual prompts, transitioning from text-only to hybrid prompts through curriculum learning.

E4SRec integrates LLMs with traditional recommender systems using item IDs to generate ranking lists efficiently.

RecInterpreter enhances LLM understanding of sequential recommenders through multimodal pairs and lightweight adapters, improving item sequence prediction.

VQ-Rec employs Vector-Quantized representations for transferable sequential recommendations, optimizing cross-domain applications.

K-LaMP leverages user interaction history with a search engine, augmenting LLM prompts with entity-centric knowledge from web activities.

One Model for All uses an LLM for multi-domain recommendations by framing tasks as next-sentence predictions, enhancing domain adaptation and performance across different model sizes.

Off-the-shelf LLM-based recommendation system

RecAgent: Simulates user behaviour in a virtual environment, enhancing the flexibility and effectiveness of recommender systems through global functions and extensive simulation tests.

MediTab: Improves medical data scalability by merging diverse tabular data without needing fine-tuning, showing notable improvements in patient outcomes predictions.

ZRRS: Addresses conditional ranking tasks using LLMs with specialised prompting, demonstrating robust zero-shot capabilities on popular datasets.

LGIR: Innovates job recommendations by aligning low-quality resumes with high-quality ones using GANs, enhancing resume completion accuracy.

MINT: Trains Narrative-Driven Recommendation models using synthetic narrative queries generated by LLMs, significantly improving retrieval model training.

PEPLER: Generates natural language explanations for recommendations using LLMs, with special training strategies to enhance explanation quality and performance.

LLM4Vis: Offers visualisation recommendations and explanations using minimal examples, refining explanations iteratively with new bootstrapping methods.

ONCE: Utilises a combination of open-source and closed-source LLMs to improve content-based recommendation systems, showing significant effectiveness.

GPT4SM: Integrates GPT embeddings into basic PLMs to enhance performance in recommendation and advertising tasks.

TransRec: Uses multi-facet identifiers and specialised data structures to connect LLMs with recommendation systems effectively.

Agent4Rec: Employs LLM-powered agents in a movie recommendation simulator, exploring real human behavior simulation and studying the filter bubble effect.

Collaborative LLMs for Recommender Systems: Merges pretrained LLMs with traditional models to tackle challenges in spurious correlations and language modeling, enhancing user-item interaction accuracy.

Personalised Recommenders Systems

PLAR (Personalised LLM-based Recommender): Uses an LLM to generate user profile keywords from user behaviour data, which then informs a retrieval module for pre-filtering candidates. This system is adaptable across various retrieval algorithms, ensuring recommendations are closely aligned with user historical behaviours.

Bridging LLMs and Domain-Specific Models for Enhanced Recommendation: Integrates domain-specific models with LLMs through a collaborative training module that allows both systems to share insights, enhancing the recommendation process by combining domain-specific user behavior patterns with the broad knowledge and reasoning capabilities of LLMs.

PAP-REC: Automates the generation of personalised prompts for recommendation tasks using gradient-based methods to explore possible prompt tokens effectively, tailoring prompts to individual user profiles to improve recommendation performance.

Health-LLM: Integrates medical expertise with LLM capabilities to enhance disease prediction and health management, using patient health reports to extract features, which are then used in a trained classifier for personalised predictions.

Personalised Music Recommendation: Implements a system using cold start embeddings and contextual bandits to personalise music recommendations, significantly increasing exposure and engagement with new music releases.

GIRL (Generative Inference and Reinforcement Learning): Applies LLMs in job recommendation by generating job descriptions from CVs and using reinforcement learning to refine the matching process, focusing on personalized job seeker experiences.

ControlRec: A contrastive prompt learning framework that uses LLMs to encode user/item IDs and natural language prompts separately, employing contrastive objectives to align and merge these features for personalized recommendations.

Fine-tuned LLMs used in recommender systems

TALLRec: A framework by Bao et al. that fine-tunes LLMs using recommendation-specific data. It integrates instruction tuning for general tasks and recommendation tuning specifically for user interactions, effectively enhancing model responsiveness to recommendation tasks.

Flan-T5: Explored by Kang et al., this model assesses different LLM sizes for predicting user ratings. The study spans zero-shot, few-shot, and fine-tuning scenarios, revealing that while LLMs lag in zero-shot tasks compared to traditional models, they excel in fine-tuned environments.

InstructRec: Developed by Zhang et al., this system frames recommendation as an instruction-following task. It employs a finely-tuned Flan-T5-XL model to rerank recommendations based on detailed, template-generated user instructions.

RecLLM: Friedman et al.'s conversational recommender system allows for dynamic recommendation refinement via dialogue with users. It uses fine-tuned LLMs to understand user preferences expressed in natural language and to provide personalized recommendations.

DEALRec: Introduced by Lin et al., this approach focuses on data efficiency by selecting key data samples for fine-tuning, reducing resource requirements and accelerating model adaptation to new user behaviours and items.

INTERS: By Zhu et al., a dataset designed to boost LLM performance in information retrieval. INTERS enriches LLM capabilities across multiple search-related tasks, significantly improving their efficiency and applicability in varied domains.

Contributions of the Paper

  1. Systematic Taxonomy: The paper categorises the roles and types of LLMs within the framework of recommender systems, providing a structured approach to understanding where and how these models can be applied.

  2. Technique Overview: It offers a detailed examination of the techniques involved in applying LLMs to recommender systems, highlighting the state-of-the-art and identifying gaps in current methodologies.

  3. Challenges in Traditional Systems: The authors discuss the limitations of traditional recommender systems and how LLMs can overcome these through enhanced language understanding and generation capabilities.

Conclusion:

The paper underscores the significant potential of LLMs to revolutionise recommender systems by providing more personalised, context-aware, and efficient recommendations.

It calls for ongoing research to further refine these models, making them more robust and adaptable to the intricate demands of modern recommender systems.

This includes continuous improvements in understanding user intents and preferences as well as developing methods to mitigate the inherent challenges of LLM implementations in this field.

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Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
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