  In this paper, we determine the appropriate decay function for item-based
collaborative filtering (CF). Instead of intuitive deduction, we introduce the
Similarity-Signal-to-Noise-Ratio (SSNR) to quantify the impacts of rated items
on current recommendations. By measuring the variation of SSNR over time, drift
in user interest is well visualized and quantified. Based on the trend changes
of SSNR, the piecewise decay function is thus devised and incorporated to build
our time-aware CF algorithm. Experiments show that the proposed algorithm
strongly outperforms the conventional item-based CF algorithm and other
time-aware algorithms with various decay functions.
