Abstract
With the increase of rich datasets from various online platforms, predicting user behavior has been one of the most active research topics. The user behavior on these online platforms includes listening to music, watching videos, purchasing products, checking-in to places, and joining online sub-communities. Predicting online user behavior is an important challenge for various applications. Personalization, recommendation systems, target advertisements are based on user behavior prediction, where user's next purchases or actions need to be predicted. In this paper, we propose a hybrid generative model that can predict user behavior considering multiple factors. While previous work has been focused on two aspects individually: predicting repeat behavior or predicting new behavior, our model considers both aspects simultaneously during the learning process. The user-specific preference component is used to capture repeat behavior patterns, while the latent group preference component is used to discover new behavior. Besides these two components, we also consider the exogenous effect, which is not captured in the former two. Our experimental results on real-world datasets show how our proposed model outperforms the state-of-the-art model.
Highlights
Predicting online user behavior is crucial in various online applications, including recommendation systems, online target advertising, and personalization systems, to name a few
We propose a hybrid generative model that considers both aspects of user behavior based on Latent Dirichlet Allocation (LDA)
HYBRID GENERATIVE MODEL FOR ONLINE USER BEHAVIOR PREDICTION The hybrid generative model we propose in this article considers three major factors that affect user behavior
Summary
Predicting online user behavior is crucial in various online applications, including recommendation systems, online target advertising, and personalization systems, to name a few These applications are generally associated with predictive mechanisms that predict the future behavior of users, such as purchasing products, checking-in to place, or selecting songs and videos. We propose a hybrid generative model that considers both aspects of user behavior based on Latent Dirichlet Allocation (LDA). LDA is introduced for text analysis, several researchers [3], [5], [17], [18] have employed this technique in recommendation tasks and achieved considerable results This method was chosen as a basis for our model.
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