Recommendation System Based on Semantic Analysis and Network Models

In this work, we implement a hybrid recommendation system for news articles that combines two primary approaches: semantic analysis via TF–IDF vectorization of headlines and Nearest Neighbors search, and network analysis using an article-similarity graph constructed from shared tags. To improve recommendation quality, rare tags were filtered out, and the number of articles per tag was capped to balance the dataset. A weighted combination of semantic and graph-based scores was also employed with parameter tuning. Precision was adopted as the evaluation metric, measuring the proportion of correctly predicted tags in the recommended articles against the ground-truth tags in the test set.