Modeling of Multilayer Multicontent Latent Tree and Its Applications

Chia Yu Lin, Yu Fang Chiu, Li Chun Wang*, Dusit Niyato

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Latent tree model (LTM) is a probabilistic tree-structured graphical model, which can reveal the hidden hierarchical causal relations among data contents and play a key role in explainable artificial intelligence. However, because current LTM modeling techniques are only suitable for single-content variable, the applications of LTMs are somewhat limited. Toward this end, a multilayer LTM (ML-LTM) is first presented to deal with the hierarchical clustering issues of multicontent variables. Second, we further develop an ML-LTM-based multicontent recommendation system. Our experiment results show that the proposed ML-LTM can achieve 90% recommendation accuracy, but the current LTM can only has 20%. Third, we propose an incremental update approach for ML-LTM that can save five-sixth updating time comparing with the whole-model retraining approach for achieving the same recommendation accuracy.

Original languageEnglish
Article number9262926
Pages (from-to)5-19
Number of pages15
JournalIEEE Transactions on Computational Social Systems
Volume8
Issue number1
DOIs
StatePublished - Feb 2021

Keywords

  • Incremental update techniques
  • latent tree model (LTM)
  • multicontent data
  • recommendation systems

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