universal-recommender | Highly configurable recommender based on PredictionIO | Recommender System library
kandi X-RAY | universal-recommender Summary
kandi X-RAY | universal-recommender Summary
universal-recommender is a Scala library typically used in Artificial Intelligence, Recommender System applications. universal-recommender has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
The Universal Recommender (UR) is a new type of collaborative filtering recommender based on an algorithm that can use data from a wide variety of user preference indicators—it is called the Correlated Cross-Occurrence algorithm. Unlike matrix factorization embodied in things like MLlib's ALS, CCO is able to ingest any number of user actions, events, profile data, and contextual information. It then serves results in a fast and scalable way. It also supports item properties for building flexible business rules for filtering and boosting recommendations and can therefor be considered a hybrid collaborative filtering and content-based recommender. Most recommenders can only use conversion events, like buy or rate. Using all we know about a user and their context allows us to much better predict their preferences.
The Universal Recommender (UR) is a new type of collaborative filtering recommender based on an algorithm that can use data from a wide variety of user preference indicators—it is called the Correlated Cross-Occurrence algorithm. Unlike matrix factorization embodied in things like MLlib's ALS, CCO is able to ingest any number of user actions, events, profile data, and contextual information. It then serves results in a fast and scalable way. It also supports item properties for building flexible business rules for filtering and boosting recommendations and can therefor be considered a hybrid collaborative filtering and content-based recommender. Most recommenders can only use conversion events, like buy or rate. Using all we know about a user and their context allows us to much better predict their preferences.
Support
Quality
Security
License
Reuse
Support
universal-recommender has a low active ecosystem.
It has 654 star(s) with 173 fork(s). There are 77 watchers for this library.
It had no major release in the last 6 months.
There are 29 open issues and 22 have been closed. On average issues are closed in 135 days. There are 7 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of universal-recommender is v0.7.3
Quality
universal-recommender has no bugs reported.
Security
universal-recommender has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
universal-recommender is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
universal-recommender releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of universal-recommender
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of universal-recommender
universal-recommender Key Features
No Key Features are available at this moment for universal-recommender.
universal-recommender Examples and Code Snippets
No Code Snippets are available at this moment for universal-recommender.
Community Discussions
Trending Discussions on universal-recommender
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 11:37PIO 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?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install universal-recommender
You must build PredictionIO with the default parameters so just run ./make-distribution this will require you to install Scala 2.11 and Python 3 (as the default Scala and Python). You can also run up to Spark 2.1.x (but not 2.2.x), ES 5.5.2 or greater (but 6.x has not been tested), Hadoop 2.6 or greater, you can get away with using older versions of services except ES must be 5.x. If you have issues getting pio to build and run send questions to the PIO mailing list.
pio export with pio < 0.12.0 =====Before upgrade!=====
pio data-delete all your old apps =====Before upgrade!=====
build and install pio 0.12.0 including all the services =====The point of no return!=====
pio app new … and pio import … any needed datasets
Mahout has speedups for the Universal Recommender's use that have not been released yet so you will have to build from source. To make this easy we have a fork hosted here, with special build instructions. Make sure you are on the "sparse-speedup" branch and follow instructions in the README.md.
the UR will not build unless this line is changed, this is expected.
download the UR from here be sure move to the 0.7.0 tag.
replace the line: resolvers += "Local Repository" at "file:///Users/pat/.custom-scala-m2/repo” with your path to the local mahout build. the UR will not build unless this line is changed, this is expected
build the UR with pio build or run the integration test to get sample data put into PIO ./examples/integration-test
pio export with pio < 0.12.0 =====Before upgrade!=====
pio data-delete all your old apps =====Before upgrade!=====
build and install pio 0.12.0 including all the services =====The point of no return!=====
pio app new … and pio import … any needed datasets
Mahout has speedups for the Universal Recommender's use that have not been released yet so you will have to build from source. To make this easy we have a fork hosted here, with special build instructions. Make sure you are on the "sparse-speedup" branch and follow instructions in the README.md.
the UR will not build unless this line is changed, this is expected.
download the UR from here be sure move to the 0.7.0 tag.
replace the line: resolvers += "Local Repository" at "file:///Users/pat/.custom-scala-m2/repo” with your path to the local mahout build. the UR will not build unless this line is changed, this is expected
build the UR with pio build or run the integration test to get sample data put into PIO ./examples/integration-test
Support
All docs for the Universal Recommender are here and are hosted at https://github.com/actionml/docs.actionml.com. If you wish to change or edit the docs make a PR to that repo.
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
