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A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments.

Aitor Almeida1, Unai Bermejo1, Aritz Bilbao1

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Summary
This summary is machine-generated.

This study explores using embeddings for user behavior modeling in intelligent environments. Different models like LSTMs, CNNs, GCNs, and transformers were compared for predicting user actions and identifying deviations.

Keywords:
attentionbehavior modelingconvolutional neural networksembeddingsgraph neural networksintelligent environmentsknowledge graphsrecurrent neural networkstransformersuser behavior prediction

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Area of Science:

  • Intelligent Systems and Human-Computer Interaction
  • Machine Learning and Artificial Intelligence
  • Ubiquitous Computing and Pervasive Environments

Background:

  • Behavior modeling is crucial for intelligent environments, aiding tasks like health monitoring, activity prediction, and energy management.
  • Accurate behavior prediction enables forecasting user evolution and detecting anomalous conduct.
  • Existing methods often require complex feature engineering for representing user actions.

Purpose of the Study:

  • To propose and evaluate the use of embeddings for representing user actions in behavior modeling.
  • To compare the effectiveness of various deep learning architectures (LSTM, CNNs, GCNs, Transformers) for behavior prediction using embeddings.
  • To assess different embedding retrofitting techniques to enhance behavior modeling performance.

Main Methods:

  • User actions were represented using embedding techniques.
  • Several deep learning models, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Graph Convolutional Networks (GCNs), and Transformers, were implemented and compared.
  • The Kasteren dataset, a benchmark for intelligent environments, was utilized for model evaluation.

Main Results:

  • The study systematically compared the performance of different model architectures in behavior prediction tasks.
  • The effectiveness of various embedding retrofitting strategies was evaluated in the context of behavior modeling.
  • Performance metrics indicated variations in suitability among the tested models for embedding-based behavior prediction.

Conclusions:

  • Embeddings offer a powerful approach for representing user actions in behavior modeling within intelligent environments.
  • The choice of model architecture significantly impacts the accuracy and effectiveness of behavior prediction.
  • Further research into embedding retrofitting can optimize behavior modeling for intelligent systems.