Item-based filtering technique is a collaborative filtering algorithm for recommendations.
Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its …

The item-based collaborative filtering technique recommends an item to the user from the
rating of k-nearest items. Generally, a random value of k is considered to find nearest …

With the growing nature of data over the internet, item-based collaborative filtering has
become a promising method in the recommendation. The two-step process of item-based …

The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses …

Recommendation systems are widely used as a reference in marketing products or
businesses. The number of choices given sometimes makes a person confused in making …

Optimizing a recommender system has complexity and challenging things, such as in
collaborative filtering. In collaborative filtering recommendation, the similarities are a critical …

With the increasing complexity of contemporary E-commerce recommender systems,
personalized health product recommendations have become a challenging task. Existing …

Recommender systems are information filtering system that overcomes excess data
problems by filtering fragments of important information from massive amount of information …

This thesis is focused on analyzing and understanding the dynamics of human mobility
using location-based data gathered through the Global Positioning System (GPS) and …

The traditional similarity algorithm in collaborative filtering mainly pay attention to the
similarity or correlation of users' ratings, lacking the consideration of difference of users' …