Recommender Systems: A Data-Driven Framework for Personalized Decision Intelligence

<ns3:p>Background Recommender systems have become inherent in personalizing experiences, especially digital experiences, across domains such as e-commerce, media, and entertainment. These systems use the user to item interactions data (how an user reacts to an item) to identify patterns that predict preference and rank content. Collaborative filtering is one of the most widely used approaches, relying on similarity between users or items to generate recommendations. Methods This study examines collaborative filtering using similarity metrics applied to a curated IMDB movie dataset. Data was preprocessed using merging metadata and ratings, encoding categorical fields, and constructing feature vectors for each movie. The primary metric to compute pairwise distances between items was Cosine similarity. An item-item recommendation engine was then created and implemented, and the output was evaluated using a movie example (the Saw 2004). Results The system produced coherent recommendations aligned with the genre and thematic characteristics of the input movie used, Saw (2004). The top-ranked films exhibited high cosine similarity scores, indicating strong vector space proximity and consistent user engagement patterns. Visual exploration of the data confirmed that the similarity-based approach captured meaningful behavioral relationships. Conclusions The findings show that a simple similarity-based collaborative filtering model can effectively identify related movies without complex model architectures. Even with lightweight feature engineering, the system generated relevant recommendations that mirror typical user preferences. This demonstrates the practicality of similarity-based methods for scalable and interpretable recommendation tasks, and highlights opportunities for future extensions using hybrid or embedding based models.</ns3:p>