The scores are the sum of dot product between the user’s history vectors and the item’s indicator vectors, with norms and boosts applied for score calculation. Search this list for more discussion. The math means the more indicators or boosts the higher the possible value. These are exactly like the scores you get back from Searching with keywords. They have a precise mathematical meaning but are only used to rank results. they are returned in a form sorted by score.
Are you trying to use the scores? On Feb 28, 2017, at 11:23 PM, Masha Zaharchenko <[email protected]> wrote: Hi, everyone! I`m currently trying to use the UR to solve the ranking problem by getting scores for the each item on the list, but results are quite ambiguous and I`m not sure how to interpret them. (I`m aware of the existence of Product Ranking Engine Template, but I`m curious if I can do it with the UR) Suppose I send the following queries: A) $ curl -H "Content-Type: application/json" -d '{ "user": "5881C656F1284616BA5D47C42F930497","item":"242234","num":1,"returnSelf":true}' http://localhost:8000/queries.json Response: {"itemScores":[{"item":"242234","score":50.42802810668945}]} B)$ curl -H "Content-Type: application/json" -d '{ "user": "5881C656F1284616BA5D47C42F930497","item":"83364","num":1,"returnSelf":true}' Response: {"itemScores":[{"item":"83364","score":51.85137176513672}]} Are these scores comparable or they only have a meaning within the limits of the query(i.e. recommending the given item as the most similar to itself)? Thanks, Maria
