Online recommenders are usually referred to those used in e-Commerce websites for suggesting a product or service out of many choices. The core technology implemented behind this ...
Collaborative Filtering (CF) aims at finding patterns in a sparse matrix of contingency. It can be used for example to mine the ratings given by users on a set of items. In this p...
Recommender systems are widely used in E-Commerce for making automatic suggestions of new items that could meet the interest of a given user. Collaborative Filtering approaches co...
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative fil...
The goal of the work in this paper is towards the incorporation of context in recommender systems in the domain of mobile applications. The approach recommends mobile applications...
Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purc...
Yunhong Zhou, Dennis M. Wilkinson, Robert Schreibe...
Recommendation systems suggest products to users. Collaborative filtering (CF) systems, which base those recommendations on a database of previous ratings by various users and pro...
Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greine...
Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated. Current collaborati...
We built a Web-based adaptive recommendation system for students to select and suggest architectural cases when they analyze "Case Study" work within the architectural de...
The Web has become a ubiquitous environment for application delivery. The originally intended idea, as a distributed system for knowledgeinterchange, has given way to organizations...