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Algorithms Aside: Recommendation As The Lens Of Life

Published: 07 September 2016 Publication History

Abstract

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.

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Cited By

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  • (2021)Recommender System: Personalizing User Experience or Scientifically Deceiving Users?Proceedings of the 2021 5th International Conference on Information System and Data Mining10.1145/3471287.3471303(138-144)Online publication date: 27-May-2021
  • (2021)The Recommendation of a Practical Guide for Doctoral Students Using Recommendation System Algorithms in the Education FieldInnovations in Smart Cities Applications Volume 410.1007/978-3-030-66840-2_19(240-254)Online publication date: 13-Feb-2021
  • (2019)Choice overload and recommendation effectiveness in related-article recommendationsInternational Journal on Digital Libraries10.1007/s00799-019-00270-7Online publication date: 27-May-2019
  • Show More Cited By

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Published In

cover image ACM Conferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
September 2016
490 pages
ISBN:9781450340359
DOI:10.1145/2959100
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2016

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Author Tags

  1. machine learning
  2. personalization
  3. recommendation engine

Qualifiers

  • Research-article

Funding Sources

  • European Union's Seventh Framework Programme (FP7/2007- 2013) under CrowdRec Grant Agreement næ 610594

Conference

RecSys '16
Sponsor:
RecSys '16: Tenth ACM Conference on Recommender Systems
September 15 - 19, 2016
Massachusetts, Boston, USA

Acceptance Rates

RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

Upcoming Conference

RecSys '24
18th ACM Conference on Recommender Systems
October 14 - 18, 2024
Bari , Italy

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Cited By

View all
  • (2021)Recommender System: Personalizing User Experience or Scientifically Deceiving Users?Proceedings of the 2021 5th International Conference on Information System and Data Mining10.1145/3471287.3471303(138-144)Online publication date: 27-May-2021
  • (2021)The Recommendation of a Practical Guide for Doctoral Students Using Recommendation System Algorithms in the Education FieldInnovations in Smart Cities Applications Volume 410.1007/978-3-030-66840-2_19(240-254)Online publication date: 13-Feb-2021
  • (2019)Choice overload and recommendation effectiveness in related-article recommendationsInternational Journal on Digital Libraries10.1007/s00799-019-00270-7Online publication date: 27-May-2019
  • (2017)New Paths in Music Recommender Systems ResearchProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109934(392-393)Online publication date: 27-Aug-2017

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