1. A method for identifying consumer items more likely to be bought by an individual user, comprising: determining, by a vehicle data system having a processor and a memory, for each individual item of a plurality of items, a probability that the individual user will select the individual item, given that the individual user has expressed interest in a baseline item, wherein the probability is determined based on: a similarity among the plurality of items based on observable features thereof; an aggregate online search behavior across multiple users; and preferences of the individual user in the baseline item, wherein the baseline item is established by the individual user; and ranking, in decreasing order, probabilities determined for the plurality of items, wherein items that are more likely to be bought by the individual user are ranked higher than items that are less likely to be bought by the individual user; wherein the method further comprises: for each paired observation associated with a baseline item i established by the individual user, determining what a first next item j is expected to be selected; examining all observations associated with the baseline item i to determine what a second next item t is expected to be selected; making a prediction as to which next item the individual user will select; comparing the prediction with an actual next item k selected by the individual user; and assigning a penalty value to the prediction if the prediction is incorrect.
1. A method for identifying consumer items more likely to be bought by an individual user, comprising: determining, by a vehicle data system having a processor and a memory, for each individual item of a plurality of items, a probability that the individual user will select the individual item, given that the individual user has expressed interest in a baseline item, wherein the probability is determined based on: a similarity among the plurality of items based on observable features thereof; an aggregate online search behavior across multiple users; and preferences of the individual user in the baseline item, wherein the baseline item is established by the individual user; and ranking, in decreasing order, probabilities determined for the plurality of items, wherein items that are more likely to be bought by the individual user are ranked higher than items that are less likely to be bought by the individual user; wherein the method further comprises: for each paired observation associated with a baseline item i established by the individual user, determining what a first next item j is expected to be selected; examining all observations associated with the baseline item i to determine what a second next item t is expected to be selected; making a prediction as to which next item the individual user will select; comparing the prediction with an actual next item k selected by the individual user; and assigning a penalty value to the prediction if the prediction is incorrect. 8. A method according to claim 1 , wherein the prediction is one of a plurality of predictions, further comprising: determining a total penalty value for all incorrect predictions in the plurality of predictions; and determining a set of weights that minimizes the total penalty.