Thesis Topic Details

Topic ID:
3117
Title:
People to People Recommendation in Social Networks
Supervisor:
Xiongcai Cai
Research Area:
Machine Learning, Information Retrieval, Artificial Intelligence
Associated Staff
Assessor:
Mike Bain
Topic Details
Status:
Active
Type:
R & D
Programs:
CS CE SE
Group Suitable:
No
Industrial:
No
Pre-requisites:
--
Description:
Predicting people that other people may like has recently
become an important task in many online social networks. Traditional collaborative filtering approachesare, such as those used in Amazon.com, are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have a dual role as both "users" and "items", e.g., both, initiating and receiving contacts. Here the assumption of active users and
passive items in traditional collaborative filtering is inapplicable. In this project, we investigate using machine learning techniques to develop accurate and robust recommender systems for people to people recommendation in Sccial Networks.
Comments:
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Past Student Reports
 
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