recommender | A recommender system using collaborative filtering | Recommender System library
kandi X-RAY | recommender Summary
kandi X-RAY | recommender Summary
A recommender system using collaborative filtering written in python.
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Top functions reviewed by kandi - BETA
- Returns a list of recommended recommendations for a user
- Load training set
- Returns the top scores for each item
- Calculates the most similar items in the movie
- Transform a dict of preferences into a dict
- Dump an object to a pickle file
- Generate similarity for item types
- Compute the similarity between two strings
- Calculate similarity scores for each user
- Load the user match file
- Load a pickle file
- Load the item match from a pickle file
recommender Key Features
recommender Examples and Code Snippets
Community Discussions
Trending Discussions on recommender
QUESTION
if we check below documentation https://www.tensorflow.org/recommenders/examples/basic_retrieval it uses underscore (_) in the integer values like for batch size . What does this underscore signifies is it same as decimal point.
...ANSWER
Answered 2022-Mar-21 at 08:37The underscores are for improved readability, but do not have any deeper meaning. See this for more information. Here is an example:
QUESTION
I have a pandas dataframe like this:
...ANSWER
Answered 2022-Mar-21 at 15:56The 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:
- 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".
- 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)
QUESTION
I have already created my service principal.
Using GitHub I need to complete all parameters below. My question is where and how can we find each one?
...ANSWER
Answered 2022-Mar-08 at 12:21There 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.
QUESTION
I'm trying to build a recommender engine based on a web store that sells house products. I'll keep this simple for the example. Using scala and Spark.
I have a dataframe that contains 4 fields.
...ANSWER
Answered 2022-Feb-25 at 13:35Hope you find this useful
QUESTION
I'm evaluating Doc2Vec for a recommender API. I wasn't able to find a decent pre-trained model, so I trained a model on the corpus, which is about 8,000 small documents.
...ANSWER
Answered 2022-Feb-11 at 18:11Without 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.
QUESTION
I'm a new user with AWS Personalize. So, I only have a few questions about recommender retraining below.
- 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?
- 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?
- 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? following this docs
that's all from my question.
...ANSWER
Answered 2022-Jan-27 at 13:03Currently, 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.
QUESTION
Distributor Registration.
I want to fill list with following information about the distributor
...ANSWER
Answered 2021-Dec-23 at 18:21You 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.
QUESTION
NOTE - Since I do need to stream loading the data instead of in memory, Please show the example using tf.data.experimental.make_csv_dataset
. Also, please show an example using my exact dataset.
I'm trying to replicate this TensorFlow Recommenders tutorial with a toy dataset. However, I'm getting this below error:
...ANSWER
Answered 2021-Nov-19 at 17:50You 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:
QUESTION
I have the following code to get API data and store it in a csv file:
...ANSWER
Answered 2021-Nov-17 at 11:21You could ask awk
to insert the loop variable; here's a simplified loop to demonstrate:
QUESTION
I am trying to follow this Python notebook. I installed Spark directly in the notebook (!pip install pyspark
), but when I do:
ANSWER
Answered 2021-Nov-17 at 08:19The 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
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Install recommender
You can use recommender like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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