Time-Aware Hierarchical Attention Recurrent Neural Networks for Multi-Criteria Recommender System

Recommendation systems are an important component for various online platforms, especially in the e-commerce domain. Recommendation systems suggest items to users using information from their past interactions such as reviews, ratings, and purchase history. Traditional recommendation systems allow users to give only a single rating for an item. Recently, deep learning approaches have been used to improve recommendation accuracy in single rating systems, but these systems do not provide enough information about user preferences for an item. Domains such as gaming, movies, and tourism enable users to give ratings on multiple criteria for an item, which makes it easier to understand user preferences compared to single rating systems. In this study, we propose a Time-Aware Hierarchical Attention Recurrent Neural Network (TAH-RNN), a deep learning-based approach designed to utilize ratings from multiple criteria. Our proposed approach helps understand the association between multiple criteria ratings and overall ratings for each user. The model integrates temporal dynamics with multi-criteria ratings by applying a Time-Aware Importance-Based Sequence Formation mechanism, which assigns importance weights to each criterion based on interaction time and enables hierarchical attention to learn their relationships over sequential user behavior. Experiments using real-world datasets (TripAdvisor, BeerAdvocate, and Skytrax Airlines) indicate that the proposed approach performs well compared to single rating systems and multiple criteria approaches across various metrics.

@article{Vankayalapati2026,
  title     = {Time-Aware Hierarchical Attention Recurrent Neural Networks for Multi-Criteria Recommender System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Manogna Vankayalapati and V Ramanjaneyulu Yannam and Sarada Korrapati and Murali Krishna Enduri},
  doi       = {10.14569/IJACSA.2026.01703104},
  url       = {https://doi.org/10.14569/IJACSA.2026.01703104}
}