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
• The Correlated Cross-Occurrence Algorithm
• The Universal Recommender Slide Deck
• Multi-domain predictive AI or how to make one thing predict another

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.

Contributions are encouraged and appreciated. Create a PR against the develop branch of the git repo. We like to keep new features general so users will not be required to change the code of the UR to make use of the new feature. We will be happy to provide guidance or help via the github PR review mechanism.

PIO-0.11.0 supports use of Elasticsearch 5.x, Spark 2.x, and Scala 2.11. The UR can be compiled for all these except ES 5.x by changing the build.sbt file. In a minor release we will provide alternative build.sbt files as examples of how to do this. ES 5 support is nearly ready in a PR and will be incorporated as soon as it is ready. ES 5 uses the REST API exclusively and so will support authentication and certain ES as a service hosts. It also has some significant performance improvements.

This is a major upgrade release with several new features. Backward compatibility with 0.5.0 is maintained. Note: We no longer have a default engine.json file so you will need to copy engine.json.template to engine.json and edit it to fit your data. See the Universal Recommender Configuration docs.

• Performance: Nearly a 40% speedup for most model calculation, and a new tuning parameter that can yield further speed improvements by filtering out unused or less useful data from model building. See minEventsPerUser in the UR configuration docs.
• Complimentary Purchase aka Item-set Recommendations: "Shopping-cart" type recommendations. Can be used for wishlists, favorites, watchlists, any list based recommendations. Used with list or user data.
• Exclusion Rules: now we have business rules for inclusion, exclusion, and boosts based on item properties.
• PredictionIO 0.11.0: Full compatibility, but no support for Elasticsearch 5, an option with PIO-0.11.0.
• New Advanced Tuning: Allows several new per indicator / event type tuning parameters for tuning model quality in a more targeted way.
• Norms Support: For large dense datasets norms are now the default for model indexing and queries. This should result in slight precision gains, so better results.
• Mahout 0.13.0 Support: the UR no longer requires a local build of Mahout.
• GPU Support: via Mahout 0.13.0 the core math of the UR now supports the use of GPUs for acceleration.
• Timeout Protection: Queries for users with very large histories could cause a timeout. We now correctly limit the amount of user history that is used as per documentation, which will all but eliminate timeouts.
• Bug Fixes: The use of blackListEvents as defined in engine.json was not working for an empty list, which should and now does disable any blacklisting except explicit item blacklists contained in the query.

• Apache PIO Compatible: The first UR version compatible with Apache PredictionIO-0.10.0-incubating. All past versions do not work and should be upgraded to this. The ActionML build of PIO is permanently deprecated since it is merged with Apache PIO.

• Fixes bug when a pio build failure triggered by the release of Apache PIO. If you have problems building v0.4.0 use this version. It is meant to be used with PredictionIO-0.9.7-aml.
• Requires a custom build of Apache Mahout: instructions on the doc site This is temporary until the next Mahout release, when we will update to 0.4.3 (uses predicitonio-0.9.7-aml) and 0.5.0 (which uses predictionio-0.10.0 from Apache)

• This version requires PredictionIO-0.9.7-aml found here.
• New tuning params are now available for each "indicator" type, making indicators with a small number of possible values much more useful—things like gender or category-preference. See docs for configuring the UR and look for the indicators parameter.
• New forms of recommendations backfill allow all items to be recommended even if they have no user events yet. Backfill types include random and user defined. See docs for configuring the UR and look for the rankings parameter.

• This version requires PredictionIO-0.9.7-aml from the ActionML repo here.
• Implements a moving time window if events: Now supports the SelfCleanedDataSource trait. Adding params to the DataSource part of engine.json allows control of de-duplication, property event compaction, and a time window of event. The time window is used to age out the oldest events. Note: this only works with the ActionML fork of PredictionIO found in the repo mentioned above.
• Parameter changed: backfillField: duration to accept Scala Duration strings. This will require changes to all engine.json files that were using the older # of seconds duration.
• Event-types used in queries: added support for indicator predictiveness testing with the MAP@k tool. This is so only certain mixes of user events are used at query time.
• Bug fix: which requires that the typeName in engine.json is required be "items", with this release the type can be any string.

• removed isEmpty calls that were taking an extremely long time to execute, results in considerable speedup. Now the vast majority of pio train time is taken up by writing to Elasticsearch. This can be optimized by creating and ES cluster or giving ES lots of memory.

• a query with no item or user will get recommendations based on popularity
• a new integration test has been added
• a regression bug where some ids were being tokenized by Elasticsearch, leading to incorrect results, was fixed. NOTE: for users with complex ids containing dashes or spaces this is an important fix.
• a dateRange in the query now takes precedence to the item attached expiration and available dates.

• date ranges attached to items will be compared to the prediction servers current data if no date is provided in the query.

• date range filters implemented
• hot/trending/popular used for backfill and when no other recommendations are returned by the query
• filters/bias < 0 caused scores to be altered in v0.1.1 fixed in this version so filters have no effect on scoring.
• the model is now hot-swapped in Elasticsearch so no downtime should be seen, in fact there is no need to run pio deploy to make the new model active.
• it is now possible to have an engine.json (call it something else) dedicated to recalculating the popularity model. This allows fast updates to popularity without recalculating the collaborative filtering model.
• Elasticsearch can now be in cluster mode

• ids are now exact matches, for v0.1.0 the ids had to be lower case and were subject to tokenizing analysis so using that version is not recommended.

• user and item based queries supported
• multiple usage events supported
• filters and boosts supported on item properties and on user or item based results.
• fast writing to Elasticsearch using Spark
• convention over configuration for queries, defaults make simple/typical queries simple and overrides add greater expressiveness.

• see the github issues list

This Software is licensed under the Apache Software Foundation version 2 license found here: http://www.apache.org/licenses/LICENSE-2.0