Time is a kind of important context information, which effect users’ interests. Generally, major temporal effects included within the baseline predictors are categorized into: An item’s popularity may change over time. For example, movies can go in and out of popularity as triggered by external events such as the appearance of an actor in a new movie. Users may change their baseline ratings over time. For example, a user who tended to rate an average movie “4 stars”, may now rate such a movie “3 stars”. This may reflect several factors including a natural drift in a user’s rating scale, the fact that ratings are given in relationship to other ratings that were given recently and also the fact that the identity of the raters within a household can change over time. In this paper, we construct a temporal influence function based on the latter.