—In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their content ratings with friends. The rating ...
Online information services have grown too large for users to navigate without the help of automated tools such as collaborative filtering, which makes recommendations to users ba...
Recommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers curr...
Philip Bonhard, Clare Harries, John D. McCarthy, M...
This paper describes our system that enables members of a social network to collaboratively annotate a shared media collection. The problem is important since online social networ...
With the phenomenal success of networking sites (e.g., Facebook, Twitter and LinkedIn), social networks have drawn substantial attention. On online social networking sites, link r...
Zhijun Yin, Manish Gupta, Tim Weninger, Jiawei Han