[Up-to-date] A curated list of resources on cold-start recommendations.
This study aims to investigate the effectiveness of three Transformers (BERT, RoBERTa, XLNet) in handling data sparsity and cold start problems in the recommender system. We present a Transformer-based hybrid recommender system that predicts missing ratings and ex- tracts semantic embeddings from user reviews to mitigate the issues.
🧠 Full-stack hybrid book recommendation system combining Collaborative Filtering and Content-Based Filtering with weighted hybrid scoring, modular data pipelines, and model persistence. Deployed via Flask with responsive HTML/CSS UI and integrated CI/CD for production-ready, scalable, and interactive recommendations.
[TKDE 2018] Code for "MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation"
A collaborative-filter-based music recommender machine
An intelligent recommender system based on deep denoising graph convolutional autoencoder and learning automata
[TMLR 2025] Node Duplication Improves Cold-start Link Prediction
Implemented rank-based recommendation system and various collaborative filtering models using Python (NumPy, Pandas, Scikit-learn). Addressed sparsity and cold start problems. Evaluated models using MAE, RMSE, and precision metrics.
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