| Abstract: | Recommender system is an effective tool to find the most relevant information for onlineusers. By analyzing the historical selection records of users, recommender system predictsthe most likely future links in the user-item network and accordingly constructs apersonalized recommendation list for each user. So far, the recommendation process ismostly investigated in static user-item networks. In this paper, we propose a model whichallows us to examine the performance of the state-of-the-art recommendation algorithms inevolving networks. We find that the recommendation accuracy in general decreases with timeif 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 therecommendation algorithm. When a hybrid recommendation algorithm is applied, we find thatthe optimal parameter gradually shifts towards the diversity-favoring recommendationalgorithm, indicating that recommendation diversity is essential to keep a high long-termrecommendation accuracy. Finally, we confirm our conclusions by studying therecommendation on networks with the real evolution data. |
