recommender | NReco Recommender is a .NET port | Recommender System library

The Pandas Documentation states:

While pivot() provides general purpose pivoting with various data types (strings, numerics, etc.), pandas also provides pivot_table() for pivoting with aggregation of numeric data

Make sure the column is numeric. Without seeing how you create trainingtaken I can't provide more specific guidance. However the following may help:

1. Make sure you handle "empty" values in that column. The Pandas guide is a very good place to start. Pandas points out that "a column of integers with even one missing values is cast to floating-point dtype".
2. If working with a dataframe, the column can be cast to a specific type via your_df.your_col.astype(int) or for your example, pd.trainingtaken.astype(int)

There are three types of service principal:

• Application
• Managed Identity
• Legacy

You can use the Enterprise applications blade in the Azure portal to list and manage the service principals in a tenant. You can see the service principal's permissions, user consented permissions, which users have done that consent, sign in information, and more.

Go to the Azure Portal, open Azure Active Directory and click the Enterprise Applications menu item under Manage.

There, find the registration for the service principal, and find the corresponding information.

To create a new clientSecret for a service principal, find the corresponding registration in App Registrations and open up the Certificates & secrets menu under Manage. From there, you can create a new secret. You cannot see values for existing secrets.

Hope you find this useful

Without seeing your training code, there could easily be errors in text prep & training. Many online code examples are bonkers wrong in their Doc2Vec training technique!

Note that min_count=1 is essentially always a bad idea with this sort of algorithm: any example suggesting that was likely from a misguided author.

Is a mere .split() also the only tokenization applied for training? (The inference list-of-tokens should be prepped the same as the training lists-of-tokens.)

How was "not very good" and "oddly even worse" evaluated? For example, did the results seem arbitrary, or in-the-right-direction-but-just-weak?

"8,000 small documents" is a bit on the thin side for a training corpus, but it somewhat depends on "how small" – a few words, a sentence, a few sentences? Moving to smaller vectors, or more training epochs, can sometimes make the best of a smallish training set - but this sort of algorithm works best with lots of data, such that dense 100d-or-more vectors can be trained.

Currently, I focus on E-Commerce data set group and use the e-commerce use-case recommender. If I use this; It can't create a campaign right?

The recommenders for domain dataset groups automatically manage the inference endpoint for you. So the step of creating a campaign is not necessary. The service handles this.

If I understand correctly this one is no need to retrain the model right? (If I use recommender above) because I read in many docs, it has only a retraining process when we use only the custom resource and create a campaign right?

Correct. Training and retraining is managed by the service for domain recommenders.

So, when I increment the new event data, the recommender will apply the new data directly for recommendations, right? If yes, that means we don't need to focus on the retraining process for the e-commerce use case right?

You can send in new event data two ways. First, an event tracker can be used to incrementally stream in new events. In this case, Personalize will use new events to adjust recommendations in near-real-time to match the user's evolving intent (retraining is not necessary for this). Personalize will also persist those new events in the incremental interactions dataset so they are included in the next retraining.

The other way you can send in new event data is with a bulk import of the interactions dataset. Since bulk imports replace the previous bulk import, your bulk files need to include all interaction history you want to train on and not just new interactions. Bulk imports of the interactions dataset are included in the next retraining.

You can use recursion to find the depth of any element in the list and a plain old count to find the number of referrers.

The following code is an implementation of that idea.

You seem to be preprocessing your data incorrectly. For example, you cannot use tf.strings.to_number to convert 00001446-da5f-4d17 into some number. It will throw an error, since the string contains more than just numbers. Also, each sample in your dataset was an array instead of a single sample: Channel 1, for example, was not 1, but [1]. This was the cause of the original problem in your question. Here is a simplified working example based on your code:

You could ask awk to insert the loop variable; here's a simplified loop to demonstrate:

The main clue to the error is in the last line

"RuntimeError: Java gateway process exited before sending its port number"

You can check an old stack overflow link below for solution

Pyspark: Exception: Java gateway process exited before sending the driver its port number