  In this paper, we explore the problem of developing personalized chatbots. A
personalized chatbot is designed as a digital chatting assistant for a user.
The key characteristic of a personalized chatbot is that it should have a
consistent personality with the corresponding user. It can talk the same way as
the user when it is delegated to respond to others' messages. We present a
retrieval-based personalized chatbot model, namely IMPChat, to learn an
implicit user profile from the user's dialogue history. We argue that the
implicit user profile is superior to the explicit user profile regarding
accessibility and flexibility. IMPChat aims to learn an implicit user profile
through modeling user's personalized language style and personalized
preferences separately. To learn a user's personalized language style, we
elaborately build language models from shallow to deep using the user's
historical responses; To model a user's personalized preferences, we explore
the conditional relations underneath each post-response pair of the user. The
personalized preferences are dynamic and context-aware: we assign higher
weights to those historical pairs that are topically related to the current
query when aggregating the personalized preferences. We match each response
candidate with the personalized language style and personalized preference,
respectively, and fuse the two matching signals to determine the final ranking
score. Comprehensive experiments on two large datasets show that our method
outperforms all baseline models.
