Context-Aware Recommendation System Using Matrix Factorization

Abstract—In a commercial field, millions of new items would be added to the daily sales field. Suggesting proposed items to users for purchasing process is a critical point. Finding the best suggestions based on the user needs and behavior increase the sales productivity. Incorporating context information in recommendations process have been accompanied by many domains and applications. Different methods and strategies have been used to find recommendations. While time is an important factor for continuously updates and changes in the user preferences, incorporating it has been proved its effectiveness to enhance recommending performance. Time-aware recommender systems (TARS) has been used in a wide range of recommendation modeling. In this proposed paper, we focus to deal with three different context-aware algorithms. First, traditional matrix-factorization using explicit ratings. Second, enhanced version after dealing with time-target as basic factors for getting the results. Last, depend on the previous version, we enhance it by shrinking weights using the mathematical decay-function algorithm to improve prediction accuracy. we build our solutions and implement them using a real dataset of commercial website as our empirical case study. From our analysis and experiment, we finally evaluate the proposed model using different metrics on measuring relative performance of enhanced TARS over traditional MF.