https://doi.org/10.1140/epjb/e2016-60509-9
Regular Article
Recommendation in evolving online networks
1 Web Sciences Center, School of
Computer Science and Engineering, University of Electronic Science and Technology of
China, Chengdu
610054, P.R.
China
2 School of Systems Science, Beijing
Normal University, Beijing
100875, P.R.
China
3 Chongqing Institute of Green and
Intelligent Technology, Chinese Academy of Sciences, Chongqing
400714, P.R.
China
a
e-mail: an.zeng@unifr.ch
Received:
26
June
2015
Received in final form:
20
December
2015
Published online:
17
February
2016
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.
Key words: Statistical and Nonlinear Physics
© EDP Sciences, Società Italiana di Fisica, Springer-Verlag, 2016