1. A computer-implemented method of recommending an item to a potential buyer, the method comprising: accessing behavior data pertinent to a first cluster of items, the first cluster representing a first product of which each item of the first cluster is a specimen, the behavior data including an event record representative of a first event type pertinent to the first product; calculating a probability based on the behavior data, the probability being of a co-occurrence of a second event type with the first event type, the second event type pertaining to a second product represented by a second cluster of items, the second cluster including the item to be recommended to the potential buyer, the calculating being performed by a module implemented using a processor of a machine; identifying the item based on the probability of the co-occurrence; and presenting a recommendation of the item to the potential buyer, the recommendation including item data descriptive of the item and indicating the item as a specimen of the second product.