Mapping research trends and predicting citations in recommender systems papers using natural language processing and machine learning

This paper presents a metadata analysis framework to investigate research trends and predict citations in the field of recommender systems. We apply Natural Language Processing (NLP) techniques, using spaCy and regex, to extract and analyze frequencies of different system types and technologies. To explore relevant researchtopics, we implement both BERTopic and Non-negative Matrix Factorization (NMF). We also introduce a zeroshot classification pipeline with the facebook/bart-large-mnli model to categorize articles into recommender system categories without requiring supervision. For citation prediction, we employ the distilbert-base-uncased model from Hugging Face to tokenize and vectorize text features, engineering 13 additional features to enrich the dataset. Random Forest, XGBoost and LightGBM models are used to evaluate predictive performance using RMSE and R² under both baseline and feature-enhanced scenarios. The proposed feature engineering and dimensionality reduction approach significantly improve citation prediction performance compared to the baseline. Specifically, RMSE is reduced from approximately 19-21 to around 6.8-10.6, while the coefficient of determination (R²) increases from 0.38-0.45 to up to 0.93 across the evaluated models. The most notable improvement is observed for the Random Forest and LightGBM models, where RMSE decreases by over 65% and R². Context-aware systems, with approximately 300 mentions and knowledge-based systems, with fewer than 100 mentions, receive comparatively less attention in the literature. BERTopic reveals a diverse and evolving landscape in recommender system research, spanning deep learning models, privacy and federated learning, domain-specific applications in tourism, education, healthcare and fake news. Explainable and cross-domain recommendations appear more prominently in recent years.