Artificial Intelligence based Recommendation System for Analyzing Social Bussiness Reviews

Recently, analysing reviews presented by clients to products that are provided by e-commerce companies, such as Amazon, to produce efficient recommendations has been receiving a lot of attention. However, ensuring and generating effective recommendations on time is a challenge. This research paper proposes an artificial intelligence-based system. The proposed system uses the Incremental Learning - based Method (ILbM) to learn a neural network classifier. The ILbM uses the bagging technique in the process of training the classifier. To ensure a high degree of performance, the ILbM is implemented on the Hadoop since it allows the execution in parallel. Compared to a similar system, the proposed system shows better results in terms of accuracy (97.5%), precision (95.7%), recall (91.5%), and time of response (36 seconds).

@article{Alanazi2021,
  title     = {Artificial Intelligence based Recommendation System for Analyzing Social Bussiness Reviews},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {6},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Asma Alanazi and Marwan Alseid},
  doi       = {10.14569/IJACSA.2021.0120614},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120614}
}