2 research outputs found
A hybrid movie recommender system and rating prediction model
In the current era, a rapid increase in data volume produces redundant information on the internet. This predicts
the appropriate items for users a great challenge in information systems. As a result, recommender systems have emerged
in this decade to resolve such problems. Various e-commerce platforms such as Amazon and Netflix prefer using some
decent systems to recommend their items to users. In literature, multiple methods such as matrix factorization and
collaborative filtering exist and have been implemented for a long time, however recent studies show that some other
approaches, especially using artificial neural networks, have promising improvements in this area of research.
In this research, we propose a new hybrid recommender system that results in better performance. In the proposed system,
the users are divided into two main categories, namely average users, and non-average users. Then, various machine
learning and deep learning methods are applied within these categories to achieve better results. Some methods such as
decision trees, support vector regression, and random forest are applied to the average users. On the other side, matrix
factorization, collaborative filtering, and some deep learning methods are implemented for non-average users. This
approach achieves better compared to the traditional methods.No sponso
Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System
The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings
of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly
influences the predicted rating, as less count of co-rated items may degrade the performance of the
collaborative filtering. However, consideration of item features to find the nearest neighbor can
be a more judicious approach to increase the proportion of similar users. In this study, we offer a
new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed
framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the
wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens
datasets and the experimental results corroborate our anticipations