Improving Web Movie Recommender System Based on Emotions

Recommender Systems (RSs) are garnering a significant importance with the advent of e-commerce and e-business on the web. This paper focused on the Movie Recommender System (MRS) based on human emotions. The problem is the MRS need to capture exactly the customer’s profile and features of movies, therefore movie is a complex domain and emotions is a human interaction domain, so difficult to combining together in the new Recommender System (RS). In this paper, we prepare a new hybrid approach for improving MRS, it consists of Content Based Filtering (CBF), Collaborative Filtering (CF), emotions detection algorithm and our algorithm, that presented by matrix. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.

@article{Wakil2015,
  title     = {Improving Web Movie Recommender System Based on Emotions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {2},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Karzan Wakil and Rebwar Bakhtyar and Karwan Ali and Kozhin Alaadin},
  doi       = {10.14569/IJACSA.2015.060232},
  url       = {https://doi.org/10.14569/IJACSA.2015.060232}
}