LogoLogo
Continuum WebsiteContinuum ApplicationsContinuum KnowledgeAxolotl Platform
Continuum Knowledge
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
Powered by GitBook
LogoLogo

Continuum - Accelerated Artificial Intelligence

  • Continuum Website
  • Axolotl Platform

Copyright Continuum Labs - 2023

On this page
  • Key Components of the Proposed System
  • The LLMRG (Large Language Model Reasoning Graphs) framework
  • Adaptive Reasoning Model
  • Chained Graph Reasoning
  • Integration with Conventional Recommender Systems
  • Performance
  • Strengths
  • Weaknesses
  • Key Lessons
  • Advantages of LLMRG
  • Summary
  • References

Was this helpful?

  1. KNOWLEDGE
  2. Retrieval Augmented Generation

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

PreviousHYDE: Revolutionising Search with Hypothetical Document EmbeddingsNextRetrieval Augmented Generation (RAG) versus fine tuning

Last updated 11 months ago

Was this helpful?

This January 2024 introduces an approach called LLM Reasoning Graphs (LLMRG) that leverages the powerful reasoning capabilities of large language models (LLMs) to construct personalised reasoning graphs.

These graphs aim to capture the higher-level semantic relationships between a user's profile, behavioural sequences, and interests in an interpretable way.

The motivation behind this research is to address the limitations of conventional recommendation systems, which often lack interpretability and fail to capture the full spectrum of conceptual relationships spanning a user's diverse interests and behaviours over time.

Even more advanced knowledge graph-based recommender systems still struggle to perform complex reasoning or deeply understand users' interests.

Key Components of the Proposed System

Personalised Reasoning Graphs: The core of the proposed method is the construction of reasoning graphs using LLMs. These graphs represent users' interests and behaviours, linking them through causal and logical inferences to offer a comprehensive view of the user's preferences.

Chained Graph Reasoning: This module applies causal and logical reasoning to construct the graphs, chaining together different concepts and user behaviours to form a coherent structure that reflects the user's interests.

Divergent Extension: This process expands the reasoning graph by exploring and associating various user interests, potentially uncovering new, relevant connections that enhance the recommendation's relevance and personalisation.

Self-Verification and Scoring: To ensure the validity of the generated reasoning chains, the system employs a self-verification mechanism that scores the chains, reinforcing the model's reliability and accuracy.

Knowledge Base Self-Improvement: The model incorporates a self-improving mechanism that caches validated reasoning chains, allowing the system to refine and enhance its reasoning capabilities over time.

The LLMRG (Large Language Model Reasoning Graphs) framework

The LLMRG (Large Language Model Reasoning Graphs) framework is designed to enhance sequential recommendation systems by leveraging the reasoning capabilities of large language models (LLMs).

The main goal is to construct personalised reasoning graphs that capture the causal and logical relationships between a user's profile, behavioural sequences, and interests.

This approach aims to provide more interpretable and insightful recommendations by going beyond simple sequential modeling of user interactions.

The LLMRG framework consists of two main components:

  1. an adaptive reasoning module with self-verification

  2. a base sequential recommendation model

The adaptive reasoning module is responsible for constructing the personalised reasoning graphs, while the base model handles the traditional sequential recommendation tasks.

Adaptive Reasoning Model

The adaptive reasoning module comprises four key components:

Chained Graph Reasoning

If no applicable links exist, it starts new chains rooted in the item itself.

The LLMs generate plausible new reasoning chains that explain the user's motivation for engaging with the next item in their sequence.

Divergent Extension

This component performs imaginary continuations of each reasoning chain to predict the next items the user is likely to engage with.

It employs an imagination engine to divergently extend the chains beyond the last known item, capturing the user's multifaceted interests.

The generated new item recommendations are then retrieved from the original item list using a small language model to calculate similarity.

Self-verification and Scoring

This component uses the abductive reasoning capability of LLMs to check the plausibility and coherence of the generated reasoning chains.

It masks key items or user attributes in the chains and prompts the LLM to fill in the masked elements.

If the predicted item or attribute matches the original, it indicates that the reasoning chain is logically consistent. A threshold score is set to judge the rationality of the reasoning, and problematic chains are filtered out or recalibrated.

Knowledge Base Self-improving

To reduce computational costs and avoid redundant work, a knowledge base is introduced to cache validated reasoning chains for later reuse.

The knowledge base retains only high-quality chains based on the scores from the self-verification and scoring module. Before conducting new reasoning, the system checks if a relevant chain already exists in the knowledge base and retrieves it instead of invoking the LLM.

Embeddings

The embeddings from the adaptive reasoning module (Eori and Ediv) and the base sequential recommendation model (Ebase) are concatenated to obtain a fused embedding (Efusion).

This fused embedding is then used to predict the next item for the user.

The LLMRG framework is designed in this way to leverage the strengths of LLMs in reasoning and inference while still benefiting from the traditional sequential recommendation models.

The chained graph reasoning and divergent extension components allow for the construction of personalised reasoning graphs that capture the complex relationships between user attributes, behavioural sequences, and interests.

The self-verification and scoring component ensures the quality and coherence of the generated reasoning chains, while the knowledge base self-improving component reduces computational costs by caching and reusing validated chains.

By combining the adaptive reasoning module with a base sequential recommendation model, LLMRG can provide more interpretable and insightful recommendations without requiring access to extra information.

This hybrid approach allows for the fusion of complementary strengths, enabling the system to capture both the sequential patterns in user behavior and the underlying logical and causal relationships that drive user interests

Integration with Conventional Recommender Systems

The reasoning graph embeddings generated by LLMRG are fed into established recommendation models like BERT4Rec, FDSA, CL4SRec, and DuoRec.

This integration allows the recommender systems to leverage the semantic and logical insights provided by the LLMs while still benefiting from the predictive power of traditional models.

Performance

The experiments conducted in this study demonstrate the effectiveness of the proposed LLMRG (Large Language Model Reasoning Graphs) approach in enhancing the performance of recommendation systems. T

Strengths

Improved performance: LLMRG consistently outperformed baseline methods across multiple datasets (ML-1M, Amazon Beauty, and Amazon Clothing) and evaluation metrics (HR@5, HR@10, NDCG@5, and NDCG@10).

This indicates that the personalised reasoning graphs constructed by LLMRG can effectively capture the complex relationships between user profiles, behavioural sequences, and interests, leading to more accurate recommendations.

Evaluation Metric Explanation

  • HR@5 and HR@10 (Hit Rate): Measures the proportion of times the true item is among the top 5 or top 10 recommendations. It's a straightforward metric that indicates the likelihood of a relevant recommendation.

  • NDCG@5 and NDCG@10 (Normalized Discounted Cumulative Gain): Evaluates the ranking quality with a focus on the position of the hit. Higher relevance items appearing earlier improve the score. NDCG is a normalised form, making it easier to compare across different datasets.

Effectiveness on datasets with rich semantic information: LLMRG showed greater improvements on the ML-1M movie dataset compared to the Amazon Beauty and Clothing product datasets. This suggests that LLMRG can better leverage its relational modeling capabilities when the items contain richer semantic information and enable more semantically logical reasoning relationships.

Importance of reasoning graphs: Ablation studies demonstrated that the reasoning graph constructed by LLMRG is critical for performance. Simply combining a base model with LLMs without constructing a reasoning graph led to minimal improvements or even decreased performance.

Synergistic effect of divergent extension and self-verification modules: The divergent extension and self-verification modules in LLMRG work synergistically to expand the search space of possible solutions while filtering out inaccurate or incoherent lines of reasoning. Removing either module led to decreased performance, highlighting their importance in the LLMRG framework.

Weaknesses

Dependence on LLM access frequency: Although the knowledge base self-improving module is designed to reduce the frequency of LLM access, LLMRG is still limited by the need for frequent LLM access for longer interaction sequences. This may lead to increased computational costs and latency in real-world applications.

Sensitivity to hyperparameters: The performance of LLMRG is sensitive to the threshold for verification scoring (Ï„) and the sequence truncation length (ltru). Improperly setting these hyperparameters can lead to decreased performance, especially for datasets with less logical sequences, such as Amazon products.

Key Lessons

  1. Integrating LLMs with reasoning capabilities into recommendation systems can significantly improve performance by capturing complex relationships between user profiles, behavioural sequences, and interests.

  2. Constructing personalised reasoning graphs can leverage the power of LLMs in recommendation systems. Simply combining base models with LLMs without reasoning graphs is insufficient.

  3. Incorporating modules that promote divergent thinking (divergent extension) and critical evaluation (self-verification) can enhance the reasoning capabilities of LLMRG and lead to more accurate and coherent recommendations.

  4. The effectiveness of LLMRG may vary depending on the nature of the dataset. Datasets with richer semantic information and more logical relationships between items (e.g., movies) may benefit more from LLMRG compared to datasets with less complex relationships (e.g., beauty and clothing products).

  5. Balancing the trade-off between the frequency of LLM access and the length of interaction sequences is important for the practical implementation of LLMRG. Techniques like knowledge base self-improving can help reduce the computational costs and latency associated with frequent LLM access.

Advantages of LLMRG

  • Interpretability: The explicit reasoning chains in the graphs provide clear insights into why certain recommendations are made, enhancing transparency and trustworthiness.

  • Enhanced Semantic Understanding: By understanding the deeper, logical connections between user behaviours and preferences, the system can make more nuanced and contextually relevant recommendations.

  • No Additional Data Required: The LLMRG approach enhances recommendation performance without needing extra user or item information, relying solely on existing behavioural data and user profiles.

Summary

The Large Language Model Reasoning Graphs (LLMRG) framework is designed to enhance recommender systems by incorporating reasoning and divergent thinking using large language models (LLMs).

LLMRG uses LLMs to construct a dynamic, graph-based representation of user interests and behaviours, which is then combined with traditional recommendation techniques to offer more personalised and forward-looking suggestions.

This approach not only improves the accuracy of recommendations but also provides a clearer rationale behind each suggestion, thanks to the interpretability of the reasoning graphs.

References

Title: Language models are few-shot learners Focus: Few-shot learning capabilities of large language models Authors: Brown, T., et al. Year: 2020 This paper discusses the ability of large language models like GPT-3 to perform a variety of tasks with minimal task-specific training examples, demonstrating their versatility and capacity for few-shot learning.

Title: Temporal meta-path guided explainable recommendation Focus: Explainable recommendation systems using meta-path guidance Authors: Chen, H., et al. Year: 2021 This research presents a framework for explainable recommendations, emphasizing the use of temporal meta-paths to guide learning processes and enhance both recommendation performance and transparency.

Title: Palm: Scaling language modeling with pathways Focus: Efficient scaling of language models Authors: Chowdhery, A., et al. Year: 2022 The study introduces the Pathways Language Model (PaLM), which aims at scaling language models more efficiently by handling diverse tasks simultaneously without compromising performance.

Title: Hierarchical capsule prediction network for marketing campaigns effect Focus: Predicting the effects of marketing campaigns using neural networks Authors: Chu, Z., et al. Year: 2022 This paper proposes a complex neural network architecture designed to predict the outcomes of marketing campaigns, improving both the predictive power and applicability in real-world marketing scenarios.

Title: Causal effect estimation: Recent advances, challenges, and opportunities Focus: Advances in causal effect estimation methods Authors: Chu, Z., et al. Year: 2023 Offers an overview of the current state of causal effect estimation, highlighting recent advancements, persistent challenges, and future opportunities in this evolving field.

Title: Bert: Pre-training of deep bidirectional transformers for language understanding Focus: Pre-training bidirectional language models for improved NLP tasks Authors: Devlin, J., et al. Year: 2018 Introduces BERT, a method that revolutionized natural language processing by using deep bidirectional transformers for pre-training, significantly enhancing performance across various tasks.

Title: Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation Focus: Enhancing sequential recommendation systems Authors: Du, H., et al. Year: 2023 This study explores the use of ensemble methods and contrastive knowledge distillation to improve the accuracy and efficiency of sequential recommendation systems.

Title: Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5) Focus: Novel framework for recommendation systems as language processing tasks Authors: Geng, S., et al. Year: 2022 Proposes a unified framework that treats recommendation systems as language processing problems, integrating pre-training, personalized prompting, and prediction to enhance recommendation quality and personalization.

Title: The movielens datasets: History and context Focus: Overview and analysis of the MovieLens datasets used in recommendation systems research Authors: Harper, F. M., and Konstan, J. A. Year: 2015 Provides detailed background and usage context of the MovieLens datasets, which are pivotal in the study and development of recommendation algorithms.

Title: Training language models to follow instructions with human feedback Focus: Improving language model adherence to instructions via human feedback Authors: Ouyang, L., et al. Year: 2022 Describes innovative methods for training language models to better follow instructions through the integration of human feedback, enhancing their practical utility in interactive applications.

Title: Self-attentive sequential recommendation Focus: Sequential recommendation using self-attention mechanisms Authors: Kang, W.-C., and McAuley, J. Year: 2018 This paper introduces a method utilizing self-attention mechanisms to improve the performance of sequential recommendation systems, demonstrating the benefit of this approach in capturing more complex user behavior patterns.

Title: BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer Focus: Applying BERT to sequential recommendation tasks Authors: Sun, F., et al. Year: 2019 This study extends the application of BERT architectures to sequential recommendation, showing how deep bidirectional transformers can be adapted to model user interaction sequences for more accurate recommendations.

Title: Knowledge-aware graph neural networks with label smoothness regularization for recommender systems Focus: Enhancing recommender systems with graph neural networks and label smoothing Authors: Wang, H., et al. Year: 2019 The research integrates knowledge graphs and label smoothness regularization into graph neural networks, improving the recommendation accuracy and the robustness of predictions.

Title: Disentangled graph collaborative filtering Focus: Collaborative filtering using graph neural networks to disentangle user-item interaction effects Authors: Wang, X., et al. Year: 2020 This paper introduces a novel method in collaborative filtering that uses graph neural networks to separate the various influencing factors in user-item interactions, thereby improving the personalization of recommendations.

Title: Chain-of-thought prompting elicits reasoning in large language models Focus: Enhancing reasoning in large language models using chain-of-thought prompts Authors: Wei, J., et al. Year: 2022 Discusses an approach to elicit more reasoned outputs from large language models through chain-of-thought prompting, demonstrating how structured prompts can guide models to produce more logical and detailed responses.

Title: Session-based recommendation with graph neural networks Focus: Improving session-based recommendation with graph neural networks Authors: Wu, S., et al. Year: 2019 This paper presents a method that applies graph neural networks to session-based recommendation, capturing complex item relations within sessions for enhanced recommendation performance.

Title: Contrastive learning for sequential recommendation Focus: Applying contrastive learning to improve sequential recommendation systems Authors: Xie, X., et al. Year: 2022 Explores the use of contrastive learning techniques to refine the embeddings used in sequential recommendation systems, leading to better discrimination of user preferences and behavior patterns.

Title: Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation From Deductive, Inductive and Abductive Views Focus: Evaluating the logical reasoning capabilities of large language models Authors: Xu, F., et al. Year: 2023 Provides a comprehensive analysis of the reasoning capabilities of large language models, assessing their performance across different types of reasoning tasks to determine their strengths and limitations.

This component constructs reasoning chains (RCn)(RCn)(RCn) that link items in the user's behavioural sequence (Su)(Su) (Su)based on logical connections.

User attributes (Au)(Au) (Au)are incorporated to further personalise the reasoning chains. The construction process is carried out iteratively along the user's behavioural sequence using a prompt-based framework with LLMs.

LogoEnhancing Recommender Systems with Large Language Model Reasoning GraphsarXiv.org
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
Page cover image