Abstract Recommender Systems (RSs) are software tools and techniques providing
suggestions for items to be of use to a user. In this introductory chapter we briefly discuss …

Abstract In the Artificial Intelligence (AI) field, and particularly within the area of Machine
Learning (ML), recommender systems have attracted significant research attention. These …

Recommender systems apply knowledge discovery techniques to the problem of making
personalized product recommendations. These systems are achieving widespread success …

Being in the digital world wherein limitless information is in the reach of everyone, people
spend enormous amount of time in various search engines to find information of their …

A method for matching a first entity with at least one second entity selected from a plurality of
second entities, comprising defining a plurality of multivalued scalar data representing …

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-
preserving collaborative filtering (PPCF) have become popular. The goal in CF is to …

Current implementations of the Collaborative Filtering (CF) algorithm are mostly centralized
and the information about users (their profiles) is stored in a single server. Centralized …

In contrast with centralized recommender systems, social recommendation algorithm is
applied to the item rating data on social networks. Meaningful recommendation can be …

In the last twelve years, the number of web user increases, so intensely leading to intense
advancement in web services which leads to enlargement the usage data at higher rates …

(57) ABSTRACT A method for matching a first entity with at least one second entity selected
from a plurality of second entities, compris ing defining a plurality of multivalued scalar data …