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## Evaluation Criteria and Experimental Procedure

We have found the following three criteria for collaborative filtering to be important: (1) the accuracy of the recommendations, (2) prediction time--the time it takes to create a recommendation list given what is known about a user, and (3) the computational resources needed to build the prediction models. We measure each of these criteria in our empirical comparison. In the remainder of this section, we describe our evaluation criterion for accuracy. Our criterion attempts to measure a user's expected utility for a list of recommendations. Of course, different users will have different utility functions. The measure we introduce provides what we believe to be a good approximation across many users. The scenario we imagine is one where a user is shown a ranked list of items and then scans that list for preferred items starting from the top. At some point, the user will stop looking at more items. Let denote the probability that a user will examine the th item on a recommendation list before stopping his or her scan, where the first position is given by . Then, a reasonable criterion is

where is 1 if the item at position is preferred and 0 otherwise. To make this measure concrete, we assume that is an exponentially decaying function:
 (9)

where is the half-life'' position--the position at which an item will be seen with probability 0.5. In our experiments, we use . In one possible implementation of this approach, we could show recommendations to a series of users and ask them to rate them as preferred'' or not preferred''. We could then use the average of cfaccuarcy(list) over all users as our criterion. Because this method is extremely costly, we instead use an approach that uses only the data we have. In particular, as already described, we randomly partition a dataset into a training set and a test set. Each case in the test set is then processed as follows. First, we randomly partition the user's preferred items into input and measurement sets. The input set is fed to the CF model, which in turn outputs a list of recommendations. Finally, we compute our criterion as
 (10)

where is the number of users in the test set, is the number of items on the recommendation list for user , is the number of preferred items in the measurement set for user , and is 1 if the th item in the recommendation list for user is preferred in the measurement set and 0 otherwise. The denominator in Equation 10 is a per-user normalization factor. It is the utility of a list where all preferred items are at the top. This normalization allows us to more sensibly combine scores across measurement sets of different size. We performed several experiments reflecting differing numbers of ratings available to the CF engines. In the first protocol, we included all but one of the preferred items in the input set. We term this protocol all but 1. In additional experiments, we placed 2, 5, and 10 preferred items in the input sets. We call these protocols given 2, given 5, and given 10. The all but 1 experiments measure the algorithms' performance when given as much data as possible from each test user. The various given experiments look at users with less data available, and examine the performance of the algorithms when there is relatively little known about an active user. When running the given m protocols, if an input set for a given user had less than preferred items, the case was eliminated from the evaluation. Thus the number of trials evaluated under each protocol varied. All experiments were performed on a 300 MHz Pentium II with 128 MB of memory running the Windows NT 4.0 operating system.

Next: Results Up: Collaborative Filtering Previous: Datasets
Journal of Machine Learning Research, 2000-10-19