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SampleRecommender | a simple kind of social recommender | Recommender System library

 by   Cascading Java Version: Current License: No License

 by   Cascading Java Version: Current License: No License

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kandi X-RAY | SampleRecommender Summary

SampleRecommender is a Java library typically used in Artificial Intelligence, Recommender System applications. SampleRecommender has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
The goal for this project is to create a sample application in [Cascading 2.7](http://www.cascading.org/) which shows how to build a simple kind of [social recommender](http://en.wikipedia.org/wiki/Recommender_system). More detailed background information and step-by-step documentation is provided at https://github.com/Cascading/SampleRecommender/wiki.
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  • SampleRecommender has a low active ecosystem.
  • It has 34 star(s) with 10 fork(s). There are 10 watchers for this library.
  • It had no major release in the last 12 months.
  • SampleRecommender has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of SampleRecommender is current.
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SampleRecommender Support
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quality kandi Quality

  • SampleRecommender has 0 bugs and 0 code smells.
SampleRecommender Quality
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SampleRecommender Quality
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securitySecurity

  • SampleRecommender has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • SampleRecommender code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
SampleRecommender Security
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SampleRecommender Security
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license License

  • SampleRecommender does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
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SampleRecommender License
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buildReuse

  • SampleRecommender releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
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Top functions reviewed by kandi - BETA

kandi has reviewed SampleRecommender and discovered the below as its top functions. This is intended to give you an instant insight into SampleRecommender implemented functionality, and help decide if they suit your requirements.

  • Main method for testing .
    • Performs a function call
      • Create tap from path .
        • Calculate the similarity of two tokens .

          Get all kandi verified functions for this library.

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          SampleRecommender Key Features

          a simple kind of social recommender

          SampleRecommender Examples and Code Snippets

          See all related Code Snippets

          Build Instructions

          copy iconCopydownload iconDownload
          gradle ideaModule

          See all related Code Snippets

          Community Discussions

          Trending Discussions on Recommender System
          • Dataframe users who did not purchase item for user-item collaborative filtering
          • How to Deploy ML Recommender System on AWS
          • What does .nonzero()[0] mean when we want to compute the sparsity of a matrix?
          • how to make an integer index corresponding to a string value?
          • How can I ensure that all users and all items appear in the training set of my recommender system?
          • LensKit Recommender only returns results for some users, otherwise returns empty DataFrame. Why is this happening?
          • How to get similarity score for unseen documents using Gensim Doc2Vec model?
          • Unable to create dataframe from RDD
          • Combining output in pandas?
          • How to get a while loop to start over after error?
          Trending Discussions on Recommender System

          QUESTION

          Dataframe users who did not purchase item for user-item collaborative filtering

          Asked 2022-Mar-05 at 12:35

          I intend to use a hybrid user-item collaborative filtering to build a Top-N recommender system with TensorFlow Keras

          currently my dataframe consist of |user_id|article_id|purchase

          user article purchases

          purchase is always TRUE because the dataset is a history of user - article purchases

          This dataset has 800,000 rows and 3 columns

          2 Questions
          1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

          2. Is a 20%, 80% true:false ratio good for this use case?

          ANSWER

          Answered 2022-Mar-05 at 12:35
          1. How do I process it such that I will have 20% purchase = true and 80% purchase = false to train the model?

          Since you only have True values, it means that you'll have to generate the False values. The only False that you know of are the user-item interactions that are not present in your table. If your known interactions can be represented as a sparse matrix (meaning, a low percentage of the possible interactions, N_ITEMS x N_USER, is present) then you can do this:

          1. Generate a random user-item combination
          2. If the user-item interaction exists, means is True, then repeat step 1.
          3. If the user-item interaction does not exist, you can consider it a False interaction.

          Now, to complete your 20%/80% part, just define the size N of the sample that you'll take from your ground truth data (True values) and take 4*N False values using the previous steps. Remember to keep some ground truth values for your test and evaluation steps.

          1. Is a 20%, 80% true:false ratio good for this use case?

          In this case, since you only have True values in your ground truth dataset, I think the best you can do is to try out different ratios. Your real world data only contains True values, but you could also generate all of the False values. The important part to consider is that some of the values that you'll consider False while training might actually be True values in your test and validation data. Just don't use all of your ground truth data, and don't generate an important portion of the possible combinations.

          I think a good start could be 50/50, then try 60/40 and so on. Evaluate using multiple metrics, see how are they changing according to the proportion of True/False values (some proportions might be better to reach higher true positive rates, other will perform worse, etc). In the end, you'll have to select one model and one training procedure according to the metrics that matter the most to you.

          Source https://stackoverflow.com/questions/71359291

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

          Vulnerabilities

          No vulnerabilities reported

          Install SampleRecommender

          To generate an IntelliJ project use:.

          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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