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. |
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Comments: |
-- | |
| Past Student Reports | ||
| Xiaoyin XU in s2, 2012 People to People Recommendation in Social Networks |
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