Real-Time Recommendation of Streamed Data

Author: Benjamin Kille

Impressions from the Tutorial

The 9th ACM Conference on Recommender Systems was held in Vienna, Austria, on 16-20 September 2015. Frank Hopfgartner (University of Glasgow), Tobias Heintz (plista GmbH), Roberto Turrin (Contentwise), and I contributed a tutorial session on real-time recommendation of streamed data. slides

The tutorial discussed a branch of recommender systems dealing with streamed data. Streams emerge as dynamic collections of users and items interact over time. Unlike traditional recommender systems, these systems reject the notion of knowing in advance users and/or items. Online content providers often refrain from requiring their users to create explicit profiles. They track users by means of session references. Additionally, editors continuously add new contents and existing contents’ relevancy reduces. Systems represent data as triples (user, item, time). They learn recommendation models on chunks of sets of triples grouped by time.

As part of the tutorial, we described the Open Recommendation Platform. ORP grants researchers access to an operative news recommender systems. Researchers can deploy a recommendation engine and connect it to ORP. Subsequently, they receive recommendation requests. ORP monitors participants’ success over time and provides graphical feedback. Participants compete with each other in CLEF NewsREEL 2016.

Finally, we introduced Idomaar. Idomaar enables researchers to re-iterate recorded streams. Thereby, they can compare various algorithms on identical data. In addition, Idomaar measures functional criteria such as scalability, complexity, and resource usage. This information reflects how well an algorithms fits requirements including response-time limits and load peaks.

More than 120 attendees showed interest in the topic. Many attendees asked questions toward the end of the session. We enjoyed engaging in fruitful discussions.