1. A method of generating personalized recommendations, the method comprising: accessing, by a recommendation system associated with a first entity and implemented by computer hardware, user activity data of a target user using a first network site associated with a second entity to determine that the target user purchased an item from the first network site, the user activity data corresponding to interactions with item data for items in an electronic catalog corresponding to the first network site, and the user activity data including interactions with item data for the purchased item; identifying, by the recommendation system, from the user activity data, seed behavior corresponding to activity by the target user with respect to the first network site, the user activity data comprising first browsing activity of the target user with respect to the electronic catalog and second browsing activity of the target user with respect to the electronic catalog, the first browsing activity corresponding to a first item category in the electronic catalog and the second browsing activity corresponding to a second item category in the electronic catalog; attempting to determine, by the recommendation system, an item type of the purchased item based on item catalog data provided by the second entity for items in the electronic catalog; in response to determining that the item type cannot be determined, filtering, by the recommendation system, the seed behavior to remove activity data for a plurality of categories including at least the first browsing activity and the second browsing activity from the seed behavior for a first time period to obtain a first filtered seed behavior; in response to determining that the item type corresponds to the first item category, filtering, by the recommendation system, the seed behavior to remove activity data corresponding to the first item category including at least the first browsing activity from the seed behavior for the first time period and a second time period that differs from the first time period, and filtering user seed behavior for a plurality of categories including at least the second browsing activity from the seed behavior for the second time period to obtain a second filtered seed behavior, the second time period comprising an abbreviated version of the first time period; and outputting a recommendation generated based at least in part on one of the first filtered seed behavior or the second filtered seed behavior.