1) A moving window: you only calculate your updates from the values in the window (say the last n reviews). The downside of this method is that older values drop off precipitously.
2) A forgetting factor: there are many possibilities but one simple one is the EWMA (exponentially weighted moving average). This is pretty standard, and takes the form
Y_updated = alpha x Y_current + (1 - alpha) x Y_previous
with alpha in [0,1]. Applying this recursively, it "forgets" older values by weight alpha. This is also known as exponential smoothing in time series. The advantage of this method is that older values are simply weighed less and drop out more gradually.
1) A moving window: you only calculate your updates from the values in the window (say the last n reviews). The downside of this method is that older values drop off precipitously.
2) A forgetting factor: there are many possibilities but one simple one is the EWMA (exponentially weighted moving average). This is pretty standard, and takes the form
with alpha in [0,1]. Applying this recursively, it "forgets" older values by weight alpha. This is also known as exponential smoothing in time series. The advantage of this method is that older values are simply weighed less and drop out more gradually.