Collaborative Filtering with Temporal Features for Movie Recommendation System

Nowadays, recommender systems play a vital role in every human being's life due to the time retrieving the items. The matrix factorization (MF) technique is one of the main methods among collaborative filtering (CF) techniques that have been widely used after the Netflix competition. Traditional MF techniques are static in nature. However, the perception and popularity of products are constantly changing with time. Similarly, the users’ tastes are changed with time. Hence, traditional MF cannot handle the dynamic effect of the user-item interaction. To tackle the temporal and dynamic effect of user-item interaction, we proposed a collaborative filtering model for movie recommendations that include temporal effects. To justify the significance of the proposed technique, we evaluated our model on a standard dataset (Movielens) and compared it with state-of-art models. The exploratory outcomes signify that the proposed technique obtains a better result than a state-of-art model with an improvement of 1.35% and 1.28% on ML-100K and 1M datasets, respectively.