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	Recommendation in evolving online networks
	Hu, Xiao1; Zeng, An2; Shang, Ming-Sheng3
	2016-02-17
摘要	Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for each user. So far, the recommendation process is mostly investigated in static user-item networks. In this paper, we propose a model which allows us to examine the performance of the state-of-the-art recommendation algorithms in evolving networks. We find that the recommendation accuracy in general decreases with time if the evolution of the online network fully depends on the recommendation. Interestingly, some randomness in users' choice can significantly improve the long-term accuracy of the recommendation algorithm. When a hybrid recommendation algorithm is applied, we find that the optimal parameter gradually shifts towards the diversity-favoring recommendation algorithm, indicating that recommendation diversity is essential to keep a high long-term recommendation accuracy. Finally, we confirm our conclusions by studying the recommendation on networks with the real evolution data.
DOI	10.1140/epjb/e2016-60509-9
发表期刊	EUROPEAN PHYSICAL JOURNAL B
ISSN	1434-6028
卷号	89期号:2页码:7
通讯作者	Zeng, A (reprint author), Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China.
收录类别	SCI
WOS记录号	WOS:000375218200002
语种	英语