User Feature Similarity Supported Collaborative Filtering for Page Recommendation Using Hybrid Shuffled Frog Leaping Algorithm

Abstract

The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to. To overcome the product overload of online shoppers, a variety of recommendation methods have been developed. Collaborative Filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations. This work proposes a recommendation methodology based on Web Usage Mining (WUM), and machine learning methods to enhance the recommendation quality and the system performance of current CF-based recommender systems. WUM populates the rating database by tracking learners’ behaviors on the Web, thereby leading to better quality recommendations. The data is collected from user profile and their preferences and also the weblink and usage. Based on the CF and WUM data, the recommendations are provided. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers is used to improve the performance of searching for nearest neighbors through Shuffled Frog Leaping Algorithm (SFLA). Experimental results show that the proposed model is effective and can enhance the performance of recommendation. Results show that the proposed SFLA-XGBoost has higher average sensitivity: rating