Co-learning Multiple Browsing Tendencies of a User by Matrix Factorization-based Multitask Learning

Abstract

Predicting an online user’s future behavior is beneficial for many applications. For example, online retailers may utilize such information to customize the marketing strategy and maximize profit. This paper aims to predict the types of webpages a user is going to click on. We observe that instead of building independent models to predict each individual type of web page, it is more effective to use a unified model to predict a user’s future clicks on different types of web pages simultaneously. The proposed model makes predictions based on the latent variables that represent possible interactions among the multiple targets and among the features. The experimental results show that this method outperforms the carefully tuned single-target training models most of the time. If the size of the training data is limited, the model shows a significant improvement over the baseline models, likely because the hidden relationship among different targets can be discovered by our model.