Why do you want to throw away user behavior in making recommendations? The lift you get in purchases will be less.
There is a use case for this when you are making recommendations basically inside a session where the user is browsing/viewing things on a hunt for something. In this case you would want to make recs using the user history of views but you have to build a model of purchase as the primary indicator or you won’t get purchase recommendations and believe me recommending views is a road to bad results. People view many things they do not buy, putting only view behavior that lead to purchases in the model. So create a model with purchase as the primary indicator and view as the secondary. Once you have the model use only the user’s session viewing history in the as the Elasticsearch query. This is a feature on our list. From: gerasimos xydas <[email protected]> <[email protected]> Reply: [email protected] <[email protected]> <[email protected]> Date: May 9, 2018 at 6:20:46 AM To: [email protected] <[email protected]> <[email protected]> Subject: UR: build/train/deploy once & querying for 3 use cases Hello everybody, We are experimenting with the Universal Recommender to provide recommendations for the 3 distinct use cases below: - Get a product recommendation based on product views - Get a product recommendation based on product purchases - Get a product recommendation based on previous purchases and views (i.e. users who viewed this bought that) The event server is fed from a single app with two types of events: "view" and "purchase". 1. How should we customize the query to fetch results for each separate case? 2. Is it feasible to build/train/deploy only once, and query for all 3 use cases? Best Regards, Gerasimos
