recommender | A recommender system using collaborative filtering | Recommender System library

 by   clasnake Python Version: Current License: MIT

kandi X-RAY | recommender Summary

kandi X-RAY | recommender Summary

recommender is a Python library typically used in Artificial Intelligence, Recommender System applications. recommender has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However recommender build file is not available. You can download it from GitHub.

A recommender system using collaborative filtering written in python.
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              recommender has a low active ecosystem.
              It has 36 star(s) with 24 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              recommender has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of recommender is current.

            kandi-Quality Quality

              recommender has 0 bugs and 0 code smells.

            kandi-Security Security

              recommender has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              recommender code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              recommender is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              recommender releases are not available. You will need to build from source code and install.
              recommender has no build file. You will be need to create the build yourself to build the component from source.
              recommender saves you 190 person hours of effort in developing the same functionality from scratch.
              It has 469 lines of code, 37 functions and 5 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed recommender and discovered the below as its top functions. This is intended to give you an instant insight into recommender implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            recommender Key Features

            No Key Features are available at this moment for recommender.

            recommender Examples and Code Snippets

            No Code Snippets are available at this moment for recommender.

            Community Discussions

            QUESTION

            Use of underscore in interger values or Number values in Tensorflow documentation
            Asked 2022-Mar-29 at 04:47

            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 08:37

            The underscores are for improved readability, but do not have any deeper meaning. See this for more information. Here is an example:

            Source https://stackoverflow.com/questions/71536910

            QUESTION

            DataError: No numeric types to aggregate pandas pivot
            Asked 2022-Mar-21 at 15:56

            I have a pandas dataframe like this:

            ...

            ANSWER

            Answered 2022-Mar-21 at 15:56

            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)

            Source https://stackoverflow.com/questions/71559906

            QUESTION

            Get the AZURE_CREDENTIALS of a Service Principal
            Asked 2022-Mar-08 at 13:28

            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 12:21

            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.

            Source https://stackoverflow.com/questions/71394641

            QUESTION

            Json in a Dataframe field in Scala in a recommender engine
            Asked 2022-Feb-25 at 13:35

            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 13:35

            Hope you find this useful

            Source https://stackoverflow.com/questions/71210116

            QUESTION

            Doc2Vec results not as expected
            Asked 2022-Feb-11 at 19:38

            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 18:11

            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.

            Source https://stackoverflow.com/questions/71083740

            QUESTION

            AWS Personalize - Recommender retraining questions
            Asked 2022-Jan-27 at 13:03

            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 13:03

            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.

            Source https://stackoverflow.com/questions/70875359

            QUESTION

            How to search Hierarchical Data with Linq in list
            Asked 2021-Dec-23 at 18:21

            Distributor Registration.

            I want to fill list with following information about the distributor

            ...

            ANSWER

            Answered 2021-Dec-23 at 18:21

            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.

            Source https://stackoverflow.com/questions/70464981

            QUESTION

            TensorFlow Recommenders - ValueError: Shape must be rank 2 but is rank 3
            Asked 2021-Nov-19 at 17:50

            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 17:50

            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:

            Source https://stackoverflow.com/questions/70024091

            QUESTION

            How to insert value at start of column in csv file using shell script
            Asked 2021-Nov-17 at 14:41

            I have the following code to get API data and store it in a csv file:

            ...

            ANSWER

            Answered 2021-Nov-17 at 11:21

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

            Source https://stackoverflow.com/questions/69989268

            QUESTION

            Runtime error when configuring Spark in Python notebook
            Asked 2021-Nov-17 at 08:19

            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 08:19

            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

            Source https://stackoverflow.com/questions/70000419

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Install recommender

            You can download it from GitHub.
            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.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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