fair-rec-sys 0.2.1

Trust-Aware Recommender System based on TBL & Fairness

⚖️ FairRecSys: Trust-Aware Fashion Recommender

Bridging Behavioral Science and AI Engineering: A Trust-Aware Recommendation Framework for the Fashion Domain.

This library is the official implementation of the paper "Trust-Aware Fashion Recommendation System via Non-Linear Review Helpfulness Analysis" (presented at 2026 ICCT).

Unlike traditional systems that optimize for simple engagement, fair-rec-sys integrates behavioral insights directly into the learning process. By operationalizing the "J-Curve" of trust, it distinguishes "substantiated validation" from "hollow positivity," ensuring recommendations are both accurate and trustworthy.

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🌟 Key Features

1. Trust-Weighted Loss (J-Curve Implementation)

• Scientific Basis: Implements the "Conditional Extremity Bias" found in our empirical study (Study 1).
• Mechanism: Contrary to the traditional Veracity Effect, our model prioritizes substantiated extremity. It dynamically assigns higher trust weights to 5-star (Decisive Validation) and 1-star (Clear Warnings) reviews, recognizing them as significantly more diagnostic than ambiguous moderate ratings (e.g., 2-star/4-star).

2. Cold-Start Mitigation for Rapid Trends

• Domain Specificity: Designed for the fashion industry's rapid trend cycles where immediate user feedback is essential.
• Solution: By utilizing a "2-core strategy" and a DistilBERT-based regressor, the system effectively handles data sparsity. It extracts trust signals from review text content even for new items with minimal interaction history, addressing the cold-start problem without relying on massive interaction logs.

3. Easy-to-Use Interface

• Bridging Science & Engineering: Provides a seamless interface to train TrustAwareBERT models with just a few lines of code. The library handles the complex operationalization of behavioral metrics into a TrustWeightedMSELoss function automatically.

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🚀 Installation

# Download from Hugging Face 
git clone [https://huggingface.co/HabinKim/trust-aware-bert-fashion](https://huggingface.co/HabinKim/trust-aware-bert-fashion)
cd trust-aware-bert-fashion
pip install .

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💻 Usage

You can train the TrustAwareBERT model with just a few lines of code. No complex preprocessing is required.

import torch
from fair_rec import TrustAwareBERT, TrustWeightedMSELoss, Trainer
from torch.utils.data import DataLoader

# 1. Initialize Model (Loads Pre-trained BERT)
model = TrustAwareBERT(pretrained_model='distilbert-base-uncased')

# 2. Define Trust-Weighted Loss (Applying J-Curve)
# This operationalizes the "Conditional Extremity Bias" (5 > 4 star trust)
criterion = TrustWeightedMSELoss(use_j_curve=True) 

# 3. Prepare DataLoader (Insert your dataset here)
# train_loader = DataLoader(...)

# 4. Start Training
# The Trainer class automatically handles the training loop
trainer = Trainer(
    model=model,
    train_loader=train_loader,   # Your actual data loader
    val_loader=None,             # Optional validation loader
    criterion=criterion,
    optimizer=torch.optim.AdamW(model.parameters(), lr=2e-5),
    device='cuda' if torch.cuda.is_available() else 'cpu'
)

# Run training for 3 epochs
trainer.train(epochs=3)

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📊 Performance

Model	NDCG@10	Fairness (Gini)	Sustainability CTR
Standard BERT	0.42	0.65 (High Bias)	2.1%
FairRecSys (Ours)	0.45	0.48 (Balanced)	5.4%

Note: Lower Gini index indicates better fairness distribution among small and large brands.The following table shows the expected performance improvements based on our internal benchmark tests.

📜 Citation

This project is licensed under the MIT License - see the LICENSE file for details.

@inproceedings{kim2026trust,
  title={Trust-Aware Fashion Recommendation: Bridging the Gap between Behavioral Helpfulness and Deep Learning},
  author={Kim, Habin},
  booktitle={Proceedings of the International Conference on Convergence Technology (ICCT)},
  year={2026}
}

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📧 Contact

• Author: Habin Kim (Ph.D.)
• Email: [e-mail]