Enhancing Collaborative Filtering Algorithm Based on Time Information.

This research focuses on the development and performance evaluation of a recommendation system model that leverages time-based data and user preference scores. The model recommends movies to users by analyzing movie data and the preference scores previously assigned by users in the system, with these scores decreasing in significance over time. The research utilizes the MovieLens dataset, which is divided into 80% for model training and 20% for testing. The model employs a Collaborative Filtering technique to determine user similarity, utilizing Pearson Correlation and Cosine Similarity methods. It then selects the nearest neighbors to the user through a two-step process: in the first step, the top K most similar neighbors are identified, and in the second step, the top 10 “friends of friends” for each neighbor are selected. The study tests K values ranging from 10 to 40, incrementing by 5 at each step. The results indicate that as the K value increases, the model's performance also improves. At a K value of 10, the model using Pearson Correlation slightly outperforms the one using Cosine Similarity, with scores of 0.53 and 0.51, respectively. When the K value increases to 15, both models perform similarly, with scores of 0.77 and 0.75, respectively. At K values of 30, 35, and 40, both models achieve equivalent performance, with scores of 0.95, 0.97, and 0.98, respectively. This research demonstrates that the developed model can be effectively applied to recommend movies or other products with similar characteristics, providing a robust foundation for time-sensitive and user-preference-based recommendation systems.