Below is a sample README.md file for your project. You can place this file in the root directory of your project.

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LLMSeqRec: An LLM-Enhanced Contextual Sequential Recommender

LLMSeqRec is an innovative framework that integrates Large Language Model (LLM)-generated semantic embeddings with traditional sequential recommendation architectures. By leveraging deep semantic representations extracted from rich textual item metadata, LLMSeqRec improves recommendation accuracy, especially in cold-start and sparse data scenarios. This project implements both the proposed LLMSeqRec and a baseline SASRec model, using the MovieLens dataset to evaluate performance.

Table of Contents

• Overview
• Folder Structure
• Requirements
• Installation
• Data Preparation
• Usage
  • Training
  • Evaluation
  • Plotting Results
• Future Scope
• Citation

Overview

LLMSeqRec enhances sequential recommendation by fusing two embedding modalities:

• LLM-based Embeddings: Pretrained semantic embeddings generated from item descriptions.
• ID-based Embeddings: Learnable collaborative embeddings.

The model employs a Transformer-based architecture (inspired by SASRec) with causal masking and multiple self-attention layers. This project includes training and evaluation scripts for both LLMSeqRec and a baseline SASRec model, alongside modules for logging and result visualization.

Folder Structure

LLMSeqRec/
├── data/
│   ├── raw/                   # Raw MovieLens dataset files
│   └── processed/             # Preprocessed CSV files (train_sequences.csv, val_sequences.csv) and LLM embeddings (llm_embeddings.npy)
├── eval/
│   ├── evaluate.py            # Evaluation script for LLMSeqRec (and baseline, if needed)
│   └── analysis.py            # Scripts for plotting training and evaluation results
├── logs/
│   ├── llmseqrec_train_log.csv
│   ├── sasrec_train_log.csv
│   ├── llmseqrec_metrics.csv
│   └── sasrec_metrics.csv
├── models/
│   ├── llmseqrec.py           # LLMSeqRec model implementation
│   └── sasrec.py              # Baseline SASRec model implementation
├── train/
│   ├── train_llmseqrec.py     # Training script for LLMSeqRec model
│   └── train_sasrec.py        # Training script for SASRec baseline
├── README.md
└── sources.bib                # BibTeX references for literature review

Requirements

• Python 3.7+
• TensorFlow 2.x
• Pandas, NumPy
• Matplotlib (for plotting)
• Additional packages as required (see requirements.txt if available)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/LLMSeqRec.git
   cd LLMSeqRec

2. (Optional) Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

   pip install -r requirements.txt

Data Preparation

1. Download the MovieLens dataset from GroupLens.
2. Place raw files in the data/raw/ folder.
3. Run the data preprocessing scripts to generate processed CSV files and compute LLM embeddings (stored in data/processed/).

Usage

Training

• To train the LLMSeqRec model:

  python -m LLMSeqRec.train.train_llmseqrec

• To train the SASRec baseline:

  python -m LLMSeqRec.train.train_sasrec

Training logs and metrics are saved in the logs/ folder.

Evaluation

• To evaluate the LLMSeqRec model on the validation set:

  python -m LLMSeqRec.eval.evaluate --val_csv "data/processed/val_sequences.csv" --emb_path "data/processed/llm_embeddings.npy" --top_k 10

• For the baseline, modify the import in the evaluation script accordingly.

Plotting Results

Generate visual comparisons of training loss and evaluation metrics:

python -m LLMSeqRec.eval.analysis

This script reads CSV logs from the logs/ folder and creates plots in the logs/ directory.

Future Scope

Future work may include integrating knowledge graphs to further enhance the semantic representation of items. By incorporating structured domain knowledge, such as genre hierarchies and actor/director relationships, the model could better capture intricate item interrelations and provide more explainable recommendations. Additionally, multimodal data such as images and reviews could be integrated to create richer, multi-dimensional embeddings. Optimizing the model for real-time recommendation and scaling it to larger, more diverse datasets also represents promising avenues for further research.

Citation

Please refer to sources.bib for detailed citations of all referenced research papers.

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This README file provides a comprehensive overview of the project, guiding users through setup, usage, and potential future enhancements. Let me know if you need further modifications!