1. A method for identifying consumer items more likely to be bought by a user, comprising: determining, by a computer communicatively connected to a computing device and responsive to the user viewing a baseline item via an interface running on the computing device, the computer having a processor and a memory, for each individual item of a plurality of items, a conditional probability that the user will select the individual item, given that the user has viewed the baseline item, wherein the conditional probability is determined based on: a similarity among the plurality of items based on observable features thereof, the plurality of items having structural differences; an aggregate online search behavior relative to individual items across multiple users; and preferences of the user with respect to the baseline item viewed by the user; ranking, in decreasing order, conditional probabilities determined for the plurality of items, wherein items that are more likely to be bought by the user are ranked higher than items that are less likely to be bought by the user; determining a conditional probability that the user will select a particular first next item after the particular baseline item is viewed by the user; and presenting, via the interface running on the computing device, at least a portion of the plurality of items ranked in decreasing order according to the conditional probabilities.