A Risk and Similarity Aware Application Recommender System

A Risk and Similarity Aware Application Recommender System

As mobile devices, especially smartphones, become more and more popular, the number of mobile applications increases dramatically. Thoughmobile applications provide users convenience and entertainment, they have potential threat to violate users’ privacy and security. In order to decrease the risk of violation, we propose a risk and similarity aware application recommender system, which recomme...

DRARS, A Dynamic Risk-Aware Recommender System

Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Similarity enhacement in Time-Aware Recommender Systems

Time-aware recommender systems (TARS) are systems that take into account a time factor the age of the user data. There are three approaches for using a time factor: (1) the user data may be given different weights by their age, (2) it may be treated as a step in a biological process and (3) it may be compared in different time frames to find a significant pattern. This research deals with the l...

merging similarity and trust based social networks to enhance the accuracy of trust-aware recommender systems

in recent years, collaborative filtering (cf) methods are important and widely accepted techniques are available for recommender systems. one of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. however, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. with the dev...