The ‘future’ magazine of the University of Siegen reported about our research, particularly about our automated algorithm selection for recommender systems.
We‘re used to streaming services suggesting new series we might like. News sites recommend articles that could interest us. Facebook shows us contacts we might know. Almost every larger website includes a recommendation system. Some systems work better than others. For the companies that use them, these systems often translate into hard cash. They can mean the difference between customer loyalty and customer losses. »Right now, companies have two options if they want to use a recommendation system,« says Prof. Dr. Jöran Beel, Professor for Intelligent Systems at the University of Siegen. »They either engage a team of experts to develop a tailor-made platform for their individual outfit, or they pay a company to host the service for them.« The big players like Netflix can afford the first option, but most other companies subscribe to a recommendation service. That‘s often a lot of money for relatively poor quality, according to the IT specialist. He aims to change that. Beel is working on a high-quality recommendation system designed for the needs of small and medium-sized enterprises. There‘s a long way to go. He and his colleagues tested five different algorithms on six news sites – for example, tagesspiegel.de or motor-talk.de. Two algorithms picked out the popular articles; two others identified users with similar preferences, then recommended the articles one user liked to another user; and a fifth algorithm suggested articles with similar content to the previously read article. When a user accessed one of the news sites, the researchers randomly selected one of the five algorithms to generate a recommendation.
For more details read the article here https://www.uni-siegen.de/presse/relaunch/publikationen/future/future-ds-2021.pdf