  With the rapid development of modern technology, the Web has become an
important platform for users to make friends and acquire information. However,
since information on the Web is over-abundant, information filtering becomes a
key task for online users to obtain relevant suggestions. As most Websites can
be ranked according to users' rating and preferences, relevance to queries, and
recency, how to extract the most relevant item from the over-abundant
information is always a key topic for researchers in various fields. In this
paper, we adopt tools used to analyze complex networks to evaluate user
reputation and item quality. In our proposed accumulative time-based ranking
(ATR) algorithm, we incorporate two behavioral weighting factors which are
updated when users select or rate items, to reflect the evolution of user
reputation and item quality over time. We showed that our algorithm outperforms
state-of-the-art ranking algorithms in terms of precision and robustness on
empirical datasets from various online retailers and the citation datasets
among research publications.
