9. A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor to implement a collaborative filter, said collaborative filter comprising: a first component configured to determine a similarity among a plurality of items based on observable features thereof; a second component configured to aggregate online search behavior across multiple users; and a third component configured to determine what features of the observable features are most important to an individual user, wherein the third component of the collaborative filter examines item discovery behavior of the individual user across multiple dimensions with respect to the observable features in a baseline item and at least one next item, wherein the baseline item is established by the individual user, wherein a probability that the individual user will select a next item, given that the user has expressed interest in the base line item, is determined based at least in part on the similarity among the plurality of items and the item discovery behavior of the individual user relative to the aggregate online search behavior across multiple users, and wherein the instructions are further translatable by the at least one processor to: for each paired observation associated with a baseline item i established by the individual user, determine what a first next item j is expected to be selected; examine all observations associated with the baseline item i to determine what a second next item t is expected to be selected; make a prediction as to which next item the individual user will select; compare the prediction with an actual next item k selected by the individual user; and assign a penalty value to the prediction if the prediction is incorrect.
9. A computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by at least one processor to implement a collaborative filter, said collaborative filter comprising: a first component configured to determine a similarity among a plurality of items based on observable features thereof; a second component configured to aggregate online search behavior across multiple users; and a third component configured to determine what features of the observable features are most important to an individual user, wherein the third component of the collaborative filter examines item discovery behavior of the individual user across multiple dimensions with respect to the observable features in a baseline item and at least one next item, wherein the baseline item is established by the individual user, wherein a probability that the individual user will select a next item, given that the user has expressed interest in the base line item, is determined based at least in part on the similarity among the plurality of items and the item discovery behavior of the individual user relative to the aggregate online search behavior across multiple users, and wherein the instructions are further translatable by the at least one processor to: for each paired observation associated with a baseline item i established by the individual user, determine what a first next item j is expected to be selected; examine all observations associated with the baseline item i to determine what a second next item t is expected to be selected; make a prediction as to which next item the individual user will select; compare the prediction with an actual next item k selected by the individual user; and assign a penalty value to the prediction if the prediction is incorrect. 11. A computer program product according to claim 9 , wherein the second component of the collaborative filter is further configured to collect item view frequencies only for each hop by the individual user in a sequence of item discovery.