Algorithms Aside: Recommendation as the Lens of Life

Tamas Motajcsek, Jean-Yves Le Moine, Martha Larson, Daniel Kohlsdorf, Andreas Lommatzsch, Domonkos Tikk, Omar Alonso, Paolo Cremonesi, Andrew Demetriou, Kristaps Dobrajs, More Authors

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.
Original languageEnglish
Title of host publicationRecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems
EditorsS. Sen, W. Geyer
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)978-1-4503-4035-9
Publication statusPublished - Sept 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States
Duration: 15 Sept 201619 Sept 2016


Conference10th ACM Conference on Recommender Systems, RecSys 2016
Country/TerritoryUnited States
CityBoston, MA
Internet address


  • Personalization
  • recommendation engine
  • machine learning


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