RecommendationEngine | Source code and dataset for paper CBMR | Recommender System library

by tinylcy Java Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | RecommendationEngine Summary

RecommendationEngine is a Java library typically used in Artificial Intelligence, Recommender System, Spark applications. RecommendationEngine has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

The key of Recommendation Engine is an efficient and scalable implementation of item-based collaborative filtering (CF) recommendation algorithm based on Hadoop.

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Quality

Security

License

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Support

RecommendationEngine has a low active ecosystem.

It has 40 star(s) with 22 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

RecommendationEngine has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of RecommendationEngine is current.

Quality

RecommendationEngine has 0 bugs and 0 code smells.

Security

RecommendationEngine has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

RecommendationEngine code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RecommendationEngine is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

RecommendationEngine 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 are not available. Examples and code snippets are available.

RecommendationEngine saves you 681 person hours of effort in developing the same functionality from scratch.

It has 1578 lines of code, 58 functions and 27 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Cat a remote file
Create file
List a folder
Download a file from remote host
Create folder
Copy a file

Get all kandi verified functions for this library.

RecommendationEngine Key Features

No Key Features are available at this moment for RecommendationEngine.

RecommendationEngine Examples and Code Snippets

No Code Snippets are available at this moment for RecommendationEngine.

Community Discussions

Trending Discussions on RecommendationEngine

deploy predictionio with spark standalone cluster

PredictionIO Training Error: ArrayIndexOutOfBoundsException

Second Autowired REST Repository not working

How to get string values in RDD while implementing spark fp growth?

QUESTION

deploy predictionio with spark standalone cluster

Asked 2018-May-22 at 10:13

I use the official Recommendation as a test. I did these steps successfully:

event server installed in a docker container.(successfully)
config eventdata, metadata and all things are stored in mysql.(successfully)
train & deploy server in another docker container.(successfully)
spark standalone cluster installed in another container.(successfully)
create new app.(successfully)
import enough eventdata.(successfully)

When I train and deploy as follows, it's ok as the docs described :

...

ANSWER

Answered 2018-May-22 at 10:13

step-deploy depends on step-train's datas written at file://tmp or hdfs://tmp.

Source https://stackoverflow.com/questions/50256449

QUESTION

PredictionIO Training Error: ArrayIndexOutOfBoundsException

Asked 2017-Jul-15 at 11:37

We have a PIO 0.11.0 instance running, and we are attempting to use the UR engine, version 0.6.0 (https://github.com/actionml/universal-recommender). We have loaded the eventserver up with our training data, and when we run pio train the following error is produced:

...

ANSWER

Answered 2017-Jul-15 at 11:37

PIO can not find any purchase events in your data (may be they have wrong format). As result it can't build correlation between users & items.

Can you show sample events?

Source https://stackoverflow.com/questions/45016344

QUESTION

Second Autowired REST Repository not working

Asked 2017-May-15 at 15:29

I am using inside of my application's controller autowired repository that works fine. But I also want to use my repo inside of another class. But when I invoke any method from the second class I get null pointer exception, looks like Autowiring is not working inside of this class. What am I doing wrong? Is it impossible to Autowire the same repo more than once? Or maybe I should implement Service and call my repo via Service?

Error:

...

ANSWER

Answered 2017-May-15 at 15:14

Q : But when I invoke any method from the second class I get null pointer exception, looks like Autowiring is not working inside of this class. What am I doing wrong?

A : It's normal, as your RecommendationEngine class is not a spring bean ==> not handled by the spring context. So to make it works you should configure it as a bean ( using annotation or xml config ).

Q : Is it impossible to Autowire the same repo more than once?

A : You can call your spring beans as many as you want.

Source https://stackoverflow.com/questions/43982843

QUESTION

How to get string values in RDD while implementing spark fp growth?

Asked 2017-Mar-28 at 20:20

I have successfully joined userID with match in the query below.

...

ANSWER

Answered 2017-Mar-28 at 20:20

You have a row with 2 fields, and row.get(2) gets the value of its third field (fields in row are 0-based, as usually); of course it's an error. To get matchesPlayedOn, use row.get(1) or just row(1).

Source https://stackoverflow.com/questions/43058171

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

Vulnerabilities

No vulnerabilities reported

Install RecommendationEngine

You can download it from GitHub.
You can use RecommendationEngine 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 RecommendationEngine 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 maven.apache.org. For Gradle installation, please refer gradle.org .