Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot

TL;DR

This paper introduces IMPChat, a retrieval-based personalized chatbot that learns implicit user profiles from dialogue history to generate responses that reflect individual user styles and preferences, outperforming existing models.

Contribution

The paper proposes a novel method to learn implicit user profiles for personalized chatbots, modeling language style and preferences separately for improved response ranking.

Findings

IMPChat outperforms baseline models on large datasets.

Implicit user profiles are more accessible and flexible than explicit profiles.

Dynamic, context-aware preference modeling enhances response relevance.

Abstract

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.