Collaborative Filtering Using Explicit and Implicit Ratings for Arabic Dataset

As the amount of digital information recorded on
the internet increases, the need for flexible recommender
systems is growing. Collaborative Filtering (CF) has been
widely used in the E-commerce industry. A variety of input data
was used, either implicitly or explicitly, to provide personalized
recommendations for specific users and helped the system to
improve its performance. Traditional CF algorithms relied
solely on users' numeric ratings to identify user preferences.
The majority of current research in recommender systems is
focusing on a single implicit or explicit rating. In this paper, we
combine explicit rating and implicit rating for user reviews to
build the best recommender system using a large Arabic
dataset. In addition, we employ two powerful techniques in the
creation of our recommender system. First, we use Item-based
CF and use cosine vector similarity to calculate the similarity
between items. Second, we use Singular Value Decomposition
(SVD) to reduce dimensionality, boost efficiency, and solve
scalability and sparsity problems in CF. The proposed
approach improves the experiment results by reducing mean
absolute and root mean squared errors. The experimental
results show to perform better when using both explicit and
implicit ratings compared with using only one type of ratings.