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Content-oriented User Modeling For Personalized Response Ranking In Chat Bots

CONTENT-ORIENTED USER MODELING FOR PERSONALIZED RESPONSE RANKING IN CHAT BOTS

  • Project Code :
  • TCMAAN139
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CONTENT-ORIENTED USER MODELING FOR PERSONALIZED RESPONSE RANKING IN CHAT BOTS

Content-Oriented User Modeling for Personalized Response Ranking in Chat bots

Abstract:-

Chat bots are tiny programs that help simulate interactions with customers automatically based on a set of predefined conditions, triggers, and/or events. There are several advantages to using chat bots: They can be proactive or reactive. Their responses are consistent every time. Generally, the semantic relevance between users' queries and the corresponding responses is considered as the essential element for conversation modeling in both generation and ranking based chat systems. By contrast, it is a non-trivial task to adopt the users' information, such as preference, social role, etc., into conversational models reasonably, while users' profiles play a significant role in the procedure of conversations by providing the implicit contexts. This app aims to address the personalized response ranking task by incorporating user profiles into the conversation model. In our approach, users' personalized representations are latently learned from the contents posted by them via a two-branch neural network. After that, a deep neural network architecture is further presented to learn the fusion representation of posts, responses and personal information.

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