  The explosive growth of information challenges people's capability in finding
out items fitting to their own interests. Recommender systems provide an
efficient solution by automatically push possibly relevant items to users
according to their past preferences. Recommendation algorithms usually embody
the causality from what having been collected to what should be recommended. In
this article, we argue that in many cases, a user's interests are stable, and
thus the previous and future preferences are highly consistent. The temporal
order of collections then does not necessarily imply a causality relationship.
We further propose a consistence-based algorithm that outperforms the
state-of-the-art recommendation algorithms in disparate real data sets,
including \textit{Netflix}, \textit{MovieLens}, \textit{Amazon} and
\textit{Rate Your Music}.
