  Predicting fine-grained interests of users with temporal behavior is
important to personalization and information filtering applications. However,
existing interest prediction methods are incapable of capturing the subtle
degreed user interests towards particular items, and the internal time-varying
drifting attention of individuals is not studied yet. Moreover, the prediction
process can also be affected by inter-personal influence, known as behavioral
mutual infectivity. Inspired by point process in modeling temporal point
process, in this paper we present a deep prediction method based on two
recurrent neural networks (RNNs) to jointly model each user's continuous
browsing history and asynchronous event sequences in the context of inter-user
behavioral mutual infectivity. Our model is able to predict the fine-grained
interest from a user regarding a particular item and corresponding timestamps
when an occurrence of event takes place. The proposed approach is more flexible
to capture the dynamic characteristic of event sequences by using the temporal
point process to model event data and timely update its intensity function by
RNNs. Furthermore, to improve the interpretability of the model, the attention
mechanism is introduced to emphasize both intra-personal and inter-personal
behavior influence over time. Experiments on real datasets demonstrate that our
model outperforms the state-of-the-art methods in fine-grained user interest
prediction.
