Recommending topics in dialogue

  • 蔡 明曄

Student thesis: Master's Thesis

Abstract

In recent years several kinds of online chat system have been developed However there exist no recommendation systems for the generation of appropriate topics for users to bring up in dialogue This paper proposes a hot-topic recommendation system to overcome this problem The proposed system analyzes the tweets of the user his chat partner and similar users as well as hashtags trending in Twitter to recommend topics The proposed system is based on the well-known algorithm Latent Dirichlet Allocation (LDA) We present a comparison of the results of the proposed system and several other commonly employed recommendation systems for a case study The proposed system outperforms the other algorithms in terms of both efficiency and accuracy
Date of Award2015 Aug 19
Original languageEnglish
SupervisorChiang Lee (Supervisor)

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