Title :
Recommend Items for User in Social Networking Services with CF
Author :
Guan, Linting ; Lu, Hailing
Author_Institution :
Collage of Math., Phys. & Inf., Zhejiang Ocean Univ., Zhoushan, China
Abstract :
In this paper we focus on the algorithm for prediction task involves predicting whether or not a user will follow an item that has been recommended to the user in social networking services. Items can be person, organizations or groups, which is sponsored by Ten cent Weibo as KDD Cup 2012. We evaluate a range of different profiling and recommendation strategies, based on a subset of large dataset from KDD Cup 2012.
Keywords :
collaborative filtering; recommender systems; social networking (online); CF; KDD Cup 2012; Tencent Weibo; recommendation strategies; recommended items; social networking services; Collaboration; Filtering; Gold; Prediction algorithms; Social network services; Sparse matrices; Training; collaborative filtering; item recommendation; recommender systems;
Conference_Titel :
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0721-5
DOI :
10.1109/CSSS.2012.340
