Paper ID

0cfdd655100055f234fd23ebecd915504b8e00e3

Title

Integrating time-aware embeddings with real-time data processing and graph augmentation to improve recommendations.

Introduction

Problem Statement

Integrating time-aware embeddings with real-time data processing and graph augmentation strategies, specifically reinforcing user-item interaction edges, will improve recommendation accuracy and user satisfaction in LLM-based recommender systems.

Motivation

Existing methods in LLM-based recommender systems often overlook the integration of temporal dynamics with real-time data processing and graph augmentation strategies. While some studies have explored dynamic updates and temporal modeling, they have not extensively tested the combined effect of time-aware embeddings with real-time data processing and graph augmentation strategies like reinforcing user-item interaction edges. This gap is crucial because it limits the system's ability to adapt to rapidly changing user preferences and interaction patterns, which are essential for maintaining high recommendation accuracy and user satisfaction. By addressing this gap, the proposed hypothesis aims to enhance the adaptability and precision of recommender systems in dynamic environments.

Proposed Method

The proposed research aims to test the hypothesis that integrating time-aware embeddings with real-time data processing and graph augmentation strategies, specifically reinforcing user-item interaction edges, will enhance recommendation accuracy and user satisfaction in LLM-based recommender systems. Time-aware embeddings will be used to capture the temporal dynamics of user preferences and item popularity, allowing the model to adapt to changes over time. Real-time data processing will ensure that the system continuously updates with the latest user interactions, maintaining the relevance and accuracy of recommendations. The graph augmentation strategy of reinforcing user-item interaction edges will be applied to strengthen the connections in the interaction graph, improving the representation of user-item relationships. This combination is expected to address the limitations of existing models by providing a more comprehensive and dynamic understanding of user preferences, leading to more accurate and satisfying recommendations. The evaluation will focus on measuring improvements in precision, recall, and user feedback scores, using a dataset that captures real-time user interactions. This approach is novel because it combines elements that have been explored individually but not in conjunction, offering a new perspective on enhancing LLM-based recommender systems.

Background

Time-Aware Embeddings: Time-aware embeddings incorporate temporal information into user and item representations, allowing the model to capture the dynamics of user preferences and item popularity over time. This variable is crucial for adapting to changes in user behavior and ensuring that recommendations remain relevant. The embeddings will be implemented using existing techniques that embed temporal features into the model's architecture. This approach was selected because it offers a way to balance historical and recent interactions, providing a more nuanced understanding of user preferences. The expected outcome is an improvement in recommendation accuracy, as the model can better account for temporal shifts in user interests.

Real-Time Data Processing: Real-time data processing involves continuously updating the recommendation model with new interaction data as it becomes available. This ensures that the recommendations reflect the most recent user behaviors and trends. The process will be implemented using streaming data architectures that support real-time data ingestion and processing. This variable was chosen because it addresses the issue of outdated recommendations in rapidly changing environments. By maintaining up-to-date user profiles and interaction data, the system is expected to deliver more accurate and timely recommendations, improving user satisfaction.

Graph Augmentation Strategy - Reinforcing User-Item Interaction Edges: This strategy involves using LLMs to strengthen the edges between users and items in the interaction graph. The process utilizes a Bayesian Personalized Ranking (BPR) sampling algorithm to identify items that users may like or dislike based on textual content. These items are then used as positive and negative samples in the BPR training process. This approach enhances the representation of user-item interactions, leading to improved recommendation accuracy. The strategy will be implemented by integrating LLMs with the interaction graph to reinforce connections based on user preferences. This variable was selected because it addresses the issue of sparse interaction data, providing a richer representation of user-item relationships.

Implementation

The proposed method involves integrating time-aware embeddings, real-time data processing, and graph augmentation strategies to enhance LLM-based recommender systems. The process begins with the implementation of time-aware embeddings, which incorporate temporal information into user and item representations. This is achieved by embedding temporal features, such as timestamps, into the model's architecture, allowing it to capture the dynamics of user preferences and item popularity over time. Next, real-time data processing is employed to continuously update the recommendation model with new interaction data. This involves setting up a streaming data architecture that supports real-time data ingestion and processing, ensuring that the system remains up-to-date with the latest user behaviors and trends. The final component is the graph augmentation strategy, specifically reinforcing user-item interaction edges. This involves using LLMs to strengthen the connections in the interaction graph, utilizing a Bayesian Personalized Ranking (BPR) sampling algorithm to identify items that users may like or dislike based on textual content. These items are used as positive and negative samples in the BPR training process, enhancing the representation of user-item interactions. The integration of these components is expected to improve recommendation accuracy and user satisfaction by providing a more comprehensive and dynamic understanding of user preferences. The evaluation will focus on measuring improvements in precision, recall, and user feedback scores, using a dataset that captures real-time user interactions. The process is designed to be feasible for implementation using the ASD agent's capabilities, with each step clearly defined and linked to the overall hypothesis.

Experiments Plan

Operationalization Information

Dynamic Graph-Enhanced Recommendations Experiment

Implement and evaluate a recommendation system that integrates time-aware embeddings with real-time data processing and graph augmentation strategies to improve recommendation accuracy and user satisfaction.

Experiment Overview

This experiment tests the hypothesis that integrating time-aware embeddings with real-time data processing and graph augmentation strategies, specifically reinforcing user-item interaction edges, will improve recommendation accuracy and user satisfaction in LLM-based recommender systems.

Pilot Experiment Settings

Implement a global variable PILOT_MODE with three possible settings: MINI_PILOT, PILOT, or FULL_EXPERIMENT. The experiment should start with MINI_PILOT mode, then proceed to PILOT mode if successful. The FULL_EXPERIMENT mode should not be run automatically (a human will verify results and manually change to this mode if needed).

Dataset Requirements

  1. Use the MovieLens-100K or MovieLens-1M dataset for this experiment, which contains user-item interactions with timestamps.
  2. Split the dataset into training (70%), validation (15%), and test (15%) sets, ensuring that the splits preserve the temporal ordering (i.e., validation and test sets contain interactions that occurred after those in the training set).
  3. For the MINI_PILOT, randomly sample a small subset from the training data.

System Components to Implement

1. Time-Aware Embeddings

Implement a time-aware embedding module that incorporates temporal information into user and item representations:

2. Simulated Real-Time Data Processing

Since we cannot implement a full streaming architecture in this experiment, simulate real-time data processing by:

3. Graph Augmentation Strategy

Implement a graph augmentation strategy that reinforces user-item interaction edges:

Baseline and Experimental Models

Implement and compare the following models:

Baseline Models

  1. Basic Matrix Factorization: A standard collaborative filtering approach without temporal information or graph enhancement.
  2. Time-Aware Matrix Factorization: Incorporates temporal information but without graph augmentation.
  3. Graph-Enhanced Matrix Factorization: Uses graph structure but without temporal awareness.

Experimental Model

Dynamic Graph-Enhanced Recommendation System: Integrates all three components - time-aware embeddings, simulated real-time processing, and graph augmentation.

Evaluation Metrics

Evaluate all models using the following metrics:

  1. Precision@K (for K=5, 10): The proportion of recommended items that are relevant.
  2. Recall@K (for K=5, 10): The proportion of relevant items that are recommended.
  3. NDCG@K (for K=5, 10): Normalized Discounted Cumulative Gain, which takes into account the ranking position of relevant items.
  4. Mean Average Precision (MAP): Average precision across all users.
  5. Simulated User Satisfaction: Since we cannot collect real user feedback, simulate user satisfaction based on the relevance of recommendations and diversity of items.

Implementation Steps

  1. Data Preparation:
  2. Load and preprocess the dataset.
  3. Split into training, validation, and test sets with temporal ordering preserved.
  4. Create user and item mappings.

  1. Model Implementation:
  2. Implement the baseline models.
  3. Implement the time-aware embedding module.
  4. Implement the simulated real-time processing module.
  5. Implement the graph augmentation strategy.
  6. Integrate all components into the experimental model.

  1. Training:
  2. Train all models on the training set.
  3. Use the validation set for hyperparameter tuning and early stopping.
  4. Save model checkpoints.

  1. Evaluation:
  2. Evaluate all models on the test set using the defined metrics.
  3. Compare performance between baseline and experimental models.
  4. Conduct statistical significance tests (e.g., paired t-tests) to determine if differences are significant.

  1. Analysis:
  2. Analyze the impact of each component (time-aware embeddings, real-time processing, graph augmentation) on recommendation performance.
  3. Visualize the user-item interaction graph before and after augmentation.
  4. Examine how recommendations change over time for specific users.

Expected Output

  1. Performance Metrics: Tables and charts comparing all models across all evaluation metrics.
  2. Statistical Analysis: Results of significance tests comparing baseline and experimental models.
  3. Visualizations:
  4. User-item interaction graphs before and after augmentation.
  5. Temporal evolution of recommendations for sample users.
  6. Embedding visualizations using dimensionality reduction techniques (e.g., t-SNE).
  7. Sample Recommendations: Examples of recommendations from each model for selected users, with explanations generated by the LLM.

Additional Requirements

  1. Implement proper logging to track experiment progress and results.
  2. Ensure reproducibility by setting random seeds.
  3. Provide clear documentation for all components and functions.
  4. Include error handling and validation checks throughout the implementation.

Please run the MINI_PILOT first to verify code functionality, then proceed to the PILOT if successful. Do not run the FULL_EXPERIMENT automatically - wait for human verification of pilot results.

End Note:

The source paper is Paper 0: Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System (324 citations, 2023). This idea draws upon a trajectory of prior work, as seen in the following sequence: Paper 1 --> Paper 2 --> Paper 3 --> Paper 4 --> Paper 5. The analysis reveals a progression from aligning LLMs with recommendation tasks to enhancing them through representation learning, graph augmentation, and instruction following. Each paper builds on the previous by addressing specific challenges like data sparsity, user interaction, and behavior comprehension. However, there remains a gap in exploring the dynamic adaptation of LLMs to evolving user preferences over time. A research idea that focuses on real-time adaptation of LLMs in recommender systems could advance the field by addressing the limitations of static models and enhancing user satisfaction.
The initial trend observed from the progression of related work highlights a consistent research focus. However, the final hypothesis proposed here is not merely a continuation of that trend — it is the result of a deeper analysis of the hypothesis space. By identifying underlying gaps and reasoning through the connections between works, the idea builds on, but meaningfully diverges from, prior directions to address a more specific challenge.

References

  1. Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System (2023)
  2. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023)
  3. Representation Learning with Large Language Models for Recommendation (2023)
  4. LLMRec: Large Language Models with Graph Augmentation for Recommendation (2023)
  5. Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023)
  6. ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation (2023)
  7. Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond (2023)
  8. Multi-Armed Bandits Meet Large Language Models (2023)
  9. Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation (2025)
  10. Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning (2024)
  11. Unsupervised Adaptation of the user Interests (2020)
  12. Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model (2020)
  13. Modeling user preference dynamics with coupled tensor factorization for social media recommendation (2020)
  14. Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning (2024)
  15. Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives (2024)
  16. Multi-objective contextual bandits in recommendation systems for smart tourism (2021)