movie-recommender | a movie recommender | Recommender System library

 by   zardosht Java Version: Current License: No License

kandi X-RAY | movie-recommender Summary

kandi X-RAY | movie-recommender Summary

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

a movie recommender

            kandi-support Support

              movie-recommender has a low active ecosystem.
              It has 4 star(s) with 2 fork(s). There are 1 watchers for this library.
              It had no major release in the last 6 months.
              movie-recommender has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of movie-recommender is current.

            kandi-Quality Quality

              movie-recommender has no bugs reported.

            kandi-Security Security

              movie-recommender has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              movie-recommender 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.

            kandi-Reuse Reuse

              movie-recommender releases are not available. You will need to build from source code and install.
              movie-recommender has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed movie-recommender and discovered the below as its top functions. This is intended to give you an instant insight into movie-recommender implemented functionality, and help decide if they suit your requirements.
            • Main method for testing
            • Evaluates the list of items based on the provided options
            • Returns a String representation of this instance
            • Run evaluation
            • Predict a rating
            • Computes the average rating
            • Calculate the similarity of two users
            • Predict a prediction
            • Calculates the similarity of a user using a list of users
            • Write the header
            Get all kandi verified functions for this library.

            movie-recommender Key Features

            No Key Features are available at this moment for movie-recommender.

            movie-recommender Examples and Code Snippets

            No Code Snippets are available at this moment for movie-recommender.

            Community Discussions


            Beginner python programmer having some issues with pandas and sklearn
            Asked 2021-Mar-05 at 20:30

            The Source of the original code is from

            The overall goal of this function is to create a content-based movie recommend system. I get that this code is creating a matrix. What I am trying to do is just print out the Title of the first movie that is recommended. My current print statement prints out the id, title, name, and type which isn't what I want and I think it has something to do with .iloc? I was thinking of just finding a way to solely get the title and add it to a list(in case I want to add more later) and then print the list, but unsure how to get just the title. As of now, the print shows:



            Answered 2021-Mar-05 at 07:53

            iloc will select the specified rows of a dataframe or a series, so the return value will still be a dataframe/series. To get the actual values you can use values to get a array of values. In this case, since you know that there will only be a single value so you can use .values[0], i.e.:



            Amazon SageMaker factorisation machine rating matrix and endpoint
            Asked 2019-Jan-23 at 10:08

            I am building a recommender system using sagemaker's built-in factorisation machine model.

            My desired result is to have a rating matrix where I can look up a predicted score by a user id and an item id.

            I understand that there is a predict API provided by the model:



            Answered 2019-Jan-23 at 10:08

            I think one could think about 2 scenarios:

            1) if you need very low latency, you can fill up the matrix indeed, i.e. compute all recos for all users, and store it in a key/value backend queried by your app. You can definitely predict multiple users at a time, using the one-hot encoded technique above.

            2) predict on-demand by invoking the endpoint directly from the app. This is quite simpler, at the cost of a little latency.

            Hope this helps.



            Optimize Cypher Query for Movie Recommendation on Large Dataset
            Asked 2018-Oct-30 at 17:03

            I'm currently working on movie recommendation using MovieLens 20m dataset after reading Node Movie connects to Genre with relationship hasGenre, Node Movie connects to User with relationship hasRating. I'm trying to retrieve all movies which are most highly co-rated (co-rated > 3.0) with a query (e.g. Toy Story) that share all genres with Toy Story. Here's my Cypher query:



            Answered 2018-Oct-30 at 17:03

            First, your Cypher can be simplified for more efficient planning by only matching what we need, and handling the rest in the WHERE (so that filtering can possibly be done while matching)



            Amazon SageMaker recommendation capabilities
            Asked 2018-Jul-12 at 04:34

            There is this example


            where the model returns the 'score' of the movie you asked for so you can recommend it or not. Can it be made to return the top 10 movies for a specific user?

            It can be done with Amazon EMR (like this ) but that solution does not offer the ease of a REST endpoint for live recommendations(I'm baffled with JobServer).



            Answered 2018-Jul-12 at 04:34

            I'm the author of this post :)

            FM will "simply" fill the missing values in the recommendation matrix. What you could do is batch-predict all movies for all users, sort the results by descending score and store the top 10 results for each user in a cache, why not. That would make it easy to retrieve results in real-time from any kind of app. I suppose you would also retrain periodically to account for new user recos.

            Hope this helps.



            TypeError for '-'
            Asked 2018-Apr-30 at 08:18

            I get this error



            Answered 2018-Apr-30 at 08:13

            You are trying to subtract an integer from a string. The error message should include the actual line in which the error occurs. If the string is indeed an integer you can convert it using int("2")

            Always try to include the full error message in a question. This makes it easiert to point to the error directly.

            Edit: since you provided the full output this is the failing line:



            kafka python - Bluemix MessageHub - ConnectionError: socket disconnected
            Asked 2017-May-20 at 12:01

            I'm using the kafka python client to push messages to Message Hub, but noticed that after a while of running my app that it would stop sending messages to Message Hub.

            I then noticed the following in my log files:



            Answered 2017-May-20 at 12:01

            As per Dominic's comment, upgrading kafka python to 1.3.3 fixed the issue for me.


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


            No vulnerabilities reported

            Install movie-recommender

            You can download it from GitHub.
            You can use movie-recommender like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the movie-recommender component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            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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