Data collection in Universal Recommender

Data collection in Universal Recommender

By : Rajdwip Majumder
Date : November 22 2020, 02:59 PM
hope this fix your issue It depends on what you want to do. If you want to be able to filter and/or boost on these, add them as properties on the item; if you want them to influence/improve the model via cross-coocurrence calcuation, send them as secondary events. [Or do both if/when it makes sense for you]
code :

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Mahout recommender - adding content-based similarity to item-based recommender

Mahout recommender - adding content-based similarity to item-based recommender

By : Arda Dura
Date : March 29 2020, 07:55 AM
should help you out Yes it's entirely reasonable to combine them. If both similarities are in [0,1], the most sensible combination is simply their product. This is something you inject using ItemSimilarity, not IDRescorer.
How much data do I need for recommender system?

How much data do I need for recommender system?

By : NewbProg
Date : March 29 2020, 07:55 AM
I wish this help you Though useless, I want to say this is not possible to give a definite number. You should focus on learning curve when adding new samples.
You can process the samples by hand and engine on parallel, and compare the result given by both. Once the measurement e.g. recall and precision of the result given by engine achieve your expectation, then you get enough samples.
PredictionIO customization Universal Recommender

PredictionIO customization Universal Recommender

By : Raj Reddy
Date : March 29 2020, 07:55 AM
I wish this helpful for you The Universal Recommender is build on the Correlated Cross-Occurrence algorithm, which measures the correlation of any number of indicators with the action to be recommended. In your case you want a user to "buy" so you should include that as the primary event—the one by which all others are compared.
The primary event is
recommender systems: Convert UUIDs to 32 bit ints for recommender libraries

recommender systems: Convert UUIDs to 32 bit ints for recommender libraries

By : Sabarish Waran
Date : March 29 2020, 07:55 AM
I wish this helpful for you This will definitely work, and is probably the solution the vast majority of users will choose. The disadvantage lies, of course, in having to maintain the mapping. A hashing function will also work. There are, in fact, approaches which use hashing to reduce the dimensionality of the embedding layers required. One thing worth bearing in mind is that the resulting hash range should be relatively compact: most implementations will allocate parameters for all possible values, so a hashing function that can hash to very large values will require exorbitant amounts of memory. Hashing following by a modulo function could work well; the trade-off then is between memory required to hold all parameters and collision probability.
In LightFM as well as most other implementations, recommendations can only be made for users and items (or at least for user and item features) that were present during the training. The mapping will then be a part of the model itself, and be effectively frozen until a new model is trained.
Is there any way to add new user to recommender system without rebuilding the model in ml.net recommender task?

Is there any way to add new user to recommender system without rebuilding the model in ml.net recommender task?

By : raph bibus
Date : March 29 2020, 07:55 AM
Does that help If you just want to predict, then no, you simply call CreatePredictionEngine passing in your user data
If you want to update your model with data for the new user, then you have to rebuild the model.
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