Adaptive User Modeling with Long and Short-Term Preferences for Personalized
Recommendation
Abstract
User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well
studied to model users’ long term preferences.
Recently, recurrent neural networks (RNN) have
shown a great advantage in modeling users’ short
term preference. A natural way to improve the recommender is to combine both long-term and shortterm modeling. Previous approaches neglect the
importance of dynamically integrating these two
user modeling paradigms. Moreover, users’ behaviors are much more complex than sentences in
language modeling or images in visual computing,
thus the classical structures of RNN such as Long
Short-Term Memory (LSTM) need to be upgraded
for better user modeling. In this paper, we improve the traditional RNN structure by proposing
a time-aware controller and a content-aware controller, so that contextual information can be well
considered to control the state transition. We further propose an attention-based framework to combine users’ long-term and short-term preferences,
thus users’ representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our
proposed method outperforms several state-of-art
methods consistently