Influential seed items recommendation

Authors:
Qi Liu;Biao Xiang;Enhong Chen;Yong Ge;Hui Xiong;Tengfei Bao;Yi Zheng
Affiliations:
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;Rutgers Business School, Rutgers University, Newark, USA;Rutgers Business School, Rutgers University, Newark, USA;School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Venue:
Proceedings of the sixth ACM conference on Recommender systems
Year:
2012

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Abstract

In this paper, we present a systematic perspective study on choosing and evaluating the initial seed items that will be recommended to the cold start users. We first construct an item consumption correlation network to capture the existing users' general consumption behaviors. Then, we formalize initial items recommendation as the influential seed set selection problem. Along this line, we present several methods, each of which selects seed items according to different rules. Finally, the experimental results on two real-world data sets verify that with different seed items, the users' consumption numbers will be quite different. Meanwhile, the results also provide many deep insights into these selection methods and their recommended seed items.