As the search progresses, Pic1 is trying to guess which area of the search space deserves more attention. In a sense, it's hypothesizing about which image the user is really searching for. Therefore, each image Pic1 displays can be thought of as a representative of a hypothesis about the target image. The actual selection the user then makes provides both positive information about that particular hypothesis and negative information about the others presented. A variety of attempts to second-guess the user are possible; here are a few:
To increase flexibility and robustness, a variety of user models may be employed. In general, many different systems for generating hypotheses about the desired image based on the sequence of user selections and, in more sophisticated modelers, state information from previous sessions with this user and other users. A simple approach would permit each of the user modeling modules a certain number of images in each presentation. Those presenting images which are chosen by the user have their allotment increased by the user model manager, creating competition and increasing the quality of subsequent presentations.
The prototype user model consists only of the simple guesser; as
It maintains a current guess and a current range of certainty .For each dimension i,
gi is initially set to the mean of the set of points,
and ri is set large enough to include all the points.
Let be the vector corresponding to the image selected
and passed in by the user interface.
Over time, is just a weighted average
of the series of selected vectors with exponential decay.
Range is a bit more subtle;
for each dimension i, ri is initialized to a value
just large enough to contain all instances in the database,
and is updated by the equation: