A curated list of papers 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.

[TKDE 2018] Code for "MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation"

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.

A collaborative-filter-based music recommender machine