This is done automatically since we know the user’s history of purchases. In fact they may have many tendencies, not just one and the UR also compares these to other users to find what similar users bought. This is the essence of collaborative filtering, finding from the data what tendencies the user has.
The UR has flexible business rules that work like you mention below but for tendencies, leave it to collaborative filtering—that is what it was invented for. For instance, you may have a “buy” event as the conversion event, and another “category-preference” event that is fired when a “buy" happens. This event might be encoded (user-id, “category-preference”, “toy”). The UR will use all the category-preference events, determine which led to “buy”s, compare them to other similar users, and conclude which items have an affinity for people with a category-preference of “toy” then at query time if the user has a category-preference of “toy” in their history the best items will be ranked in recommendations accordingly. It may be likely that the user has more than one category preference so we choose the best items using all indicators, other buys, other category-preferences, etc. On May 2, 2017, at 10:53 PM, Lin Amy <[email protected]> wrote: Hello everyone, I would like to use user property for recommendation and need some help, since it doesn't seem available with current version of "universal recommender". For example, say user A has property "tendency": ["toy"], and the user A is specified in the query, so I expect the engine to recommend items with "tendency": "toy" by given bias. Is there any way to do this? Thank you so much for any advices. Best regards, Amy
