The effect of heterogeneous dynamics of online users on information filtering

• We study the effect of heterogeneous dynamics of users on recommendation.
• Due to the user heterogeneity, their amount of links in the probe set is different.
• The personalized algorithm implementation improves the recommendation performance.
• Our results suggest different update frequency for users – recommendation list.

The rapid expansion of the Internet requires effective information filtering techniques to extract the most essential and relevant information for online users. Many recommendation algorithms have been proposed to predict the future items that a given user might be interested in. However, there is an important issue that has always been ignored so far in related works, namely the heterogeneous dynamics of online users. The interest of active users changes more often than that of less active users, which asks for different update frequency of their recommendation lists. In this paper, we develop a framework to study the effect of heterogeneous dynamics of users on the recommendation performance. We find that the personalized application of recommendation algorithms results in remarkable improvement in the recommendation accuracy and diversity. Our findings may help online retailers make better use of the existing recommendation methods.