Recommendation System Based on Prediction of User Preference Changes

Time always exists in our lives and time data can easily be collected in a variety of applications. For example, when you purchase items online or click on an ad, the time at which you chose the item or clicked the ad is recorded. The analysis of time information can therefore be applied in various areas. It is important to note that user preferences change over time. For example, a person who watched animated TV shows in childhood will most likely switch to watching the news in adulthood. It is effective to incorporate such changes into recommender systems. In this paper, we propose an approach that predicts user preferences with consideration of preference changes by learning the order of purchase history in a recommender system. Our approach is composed of three steps. First, we obtain user features based on matrix factorization and purchasing time. Next, we use a Kalman filter to predict user preference vectors from user features. Finally, we generate a recommendation list, at which time we propose two types of recommendation methods using the predicted vectors. We then show through experiments using a real-world dataset that our approach outperforms competitive methods such as the first order Markov model.