short-paper

Will recommenders kill search?: recommender systems - an industry perspective

Authors:

Ido Guy,

Henrik SchinzelAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 7 - 12

https://doi.org/10.1145/1864708.1864713

Published: 26 September 2010 Publication History

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Abstract

At the 2010 annual ACM Conference on Recommender Systems (RecSys 2010) a panel addressed emerging topics regarding recommender systems as a whole and specifically their role in industry. This report summarizes answers from a distinguished group of industry leaders representing different industries in which recommender systems are highly relevant. Panel members discuss questions regarding the role of recommender systems in their own industry area, killer applications, opportunities, and future directions.

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References

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Kumar MRani TRakesh USunitha DKumar G(2024)An Edge Artificial Intelligence Federated Recommender System for Virtual ClassroomsModel Optimization Methods for Efficient and Edge AI10.1002/9781394219230.ch17(341-349)Online publication date: 22-Nov-2024
https://doi.org/10.1002/9781394219230.ch17
Trichur Narayanan R(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
https://dl.acm.org/doi/10.1145/3471287.3471303
Chen JWang CZhou SShi QFeng YChen C(2019)SamWalker: Social Recommendation with Informative Sampling StrategyThe World Wide Web Conference10.1145/3308558.3313582(228-239)Online publication date: 13-May-2019
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Index Terms

Will recommenders kill search?: recommender systems - an industry perspective
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

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 ACM 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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Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Kumar MRani TRakesh USunitha DKumar G(2024)An Edge Artificial Intelligence Federated Recommender System for Virtual ClassroomsModel Optimization Methods for Efficient and Edge AI10.1002/9781394219230.ch17(341-349)Online publication date: 22-Nov-2024
https://doi.org/10.1002/9781394219230.ch17
Trichur Narayanan R(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
https://dl.acm.org/doi/10.1145/3471287.3471303
Chen JWang CZhou SShi QFeng YChen C(2019)SamWalker: Social Recommendation with Informative Sampling StrategyThe World Wide Web Conference10.1145/3308558.3313582(228-239)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313582
Guy IPera SEkstrand MAmatriain XO'Donovan J(2018)Connecting sellers and buyers on the world's largest inventoryProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241733(490-491)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3241733
Guo KXu TTang YZhang RMa J(2018)An Incremental Collaborative Filtering Based Recommendation Framework for Personalized Websites2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00041(181-186)Online publication date: Aug-2018
https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00041
Malhotra DRishi O(2018)An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analyticsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2018.02.015Online publication date: Mar-2018
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Guy I(2018)People Recommendation on Social MediaSocial Information Access10.1007/978-3-319-90092-6_15(570-623)Online publication date: 3-May-2018
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Zhang NMei THua XGuan LLi S(2015)TapTellJournal of Visual Communication and Image Representation10.1016/j.jvcir.2015.02.00729:C(114-124)Online publication date: 1-May-2015
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Rana CJain S(2015)A study of the dynamic features of recommender systemsArtificial Intelligence Review10.1007/s10462-012-9359-643:1(141-153)Online publication date: 1-Jan-2015
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Shi YLarson MHanjalic A(2014)Collaborative Filtering beyond the User-Item MatrixACM Computing Surveys10.1145/255627047:1(1-45)Online publication date: 1-May-2014
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Abstract

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Index Terms

Recommendations

Improving Collaborative Filtering Based Recommenders Using Topic Modelling

Integrating collaborative filtering and matching-based search for product recommendations

A Scalable, Accurate Hybrid Recommender System

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