Re: UR evaluation

Exactly, ranking is the only task of a recommender. Precision is not automatically good at that but something like MAP@k is. From: Marco Goldin Date: May 10, 2018 at 10:09:22 PM To: Pat Ferrel Cc:

Re: UR evaluation

Very nice article. And it gets much clearer the importance of treating the recommendation like a ranking task. Thanks Il gio 10 mag 2018, 19:12 Pat Ferrel ha scritto: > Here is a discussion of how we used it for tuning with multiple input > types: >

Re: UR evaluation

Here is a discussion of how we used it for tuning with multiple input types:  https://developer.ibm.com/dwblog/2017/mahout-spark-correlated-cross-occurences/ We used video likes, dislikes, and video metadata to increase our MAP@k by 26% eventually. So this was mainly an exercise in incorporating

Re: UR evaluation

You can if you want but we have external tools for the UR that are much more flexible. The UR has tuning that can’t really be covered by the built in API. https://github.com/actionml/ur-analysis-tools They do MAP@k as well as creating a bunch of other metrics and comparing different types of input

UR evaluation

hi all, i successfully trained a universal recommender but i don't know how to evaluate the model. Is there a recommended way to do that? I saw that *predictionio-template-recommender* actually has the Evaluation.scala file which uses the class *PrecisionAtK *for the metrics. Should i use this

RE: UR: build/train/deploy once & querying for 3 use cases

Hi all, to elaborate on these cases, the purpose is to create a UR for the cases of: 1. “User who Viewed this item also Viewed” 2. “User who Bought this item also Bought” 3. “User who Viewed this item also Bought ” while having Events of Buying and Viewing a product. I would