A Review-based Context-Aware Recommender Systems: Using Custom NER and Factorization Machines

Recommender Systems depend fundamentally on user feedback to provide recommendation. Classical Recom-menders are based only on historical data and also suffer from several problems linked to the lack of data such as sparsity. Users’ reviews represent a massive amount of valuable and rich knowledge information, but they are still ignored by most of current recommender systems. Information such as users’ preferences and contextual data could be extracted from reviews and integrated into Recommender Systems to provide more accurate recommendations. In this paper, we present a Context Aware Recommender System model, based on a Bidirectional Encoder Representations from Transformers (BERT) pretrained model to customize Named Entity Recognition (NER). The model allows to automatically extract contextual information from reviews then insert extracted data into a Contextual Machine Factorization to compte and predict ratings. Empirical results show that our model improves the quality of recommendation and outperforms existing Recommender Systems.

@article{Madani2022,
  title     = {A Review-based Context-Aware Recommender Systems: Using Custom NER and Factorization Machines},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Rabie Madani and Abderrahmane Ez-zahout},
  doi       = {10.14569/IJACSA.2022.0130365},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130365}
}