ExMrec2vec: Explainable Movie Recommender System based on Word2vec

According to the user profile, a recommender system intends to offer items to the user that may interest him. The recommendations have been applied successfully in various fields. Recommended items include movies, books, travel and tourism services, friends, research articles, research queries, and much more. Hence the presence of recommender systems in many areas, in particular, movies recommendations. Most current Machine Learning recommender systems serve as black boxes that do not provide the user with any insight into or justification for the system's logic. What puts users at risk of losing their confidence. Recommender systems suffer from an overload of information, which poses numerous problems, including high cost, slow data processing, and low time complexity. That is why researchers in have been using graph embeddings algorithms in the recommendation field to reduce the quantity of data, as these algorithms have been successful in the last few years. This work aims to improve the quality of recommendation and the simplicity of recommendation explanation based on the word2vec graph embeddings model.

@article{SAMIH2021,
  title     = {ExMrec2vec: Explainable Movie Recommender System based on Word2vec},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {8},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Amina SAMIH and Abderrahim GHADI and Abdelhadi FENNAN},
  doi       = {10.14569/IJACSA.2021.0120876},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120876}
}