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

Get Access

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.

Supplementary Material

JPG File (recsys2010-27092010-01-01.jpg)

Download
8.80 KB

Panel presentations (recsys2010-27092010-01-01.mov)

Download
115.26 MB

References

[1]

]]Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6 (Jun. 2005), 734--749.

Digital Library

Google Scholar

[2]

]]Balabanović, M. and Shoham, Y. 1997. Fab: content-based, collaborative recommendation. Commun. ACM 40, 3 (Mar. 1997), 66--72.

Digital Library

Google Scholar

[3]

]]Burke, R. 2002. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 4 (Nov. 2002), 331--370.

Digital Library

Google Scholar

[4]

]]Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM 35, 12 (Dec. 1992), 61--70.

Digital Library

Google Scholar

[5]

]]Guy, I., Chen, L., and Zhou, M. X. 2010. Workshop on social recommender systems. Proc. IUI '10, 433--434.

Digital Library

Google Scholar

[6]

]]Kautz, H., Selman, B., and Shah, M. 1997. Referral Web: combining social networks and collaborative filtering. Commun. ACM 40, 3 (Mar. 1997), 63--65.

Digital Library

Google Scholar

[7]

]]Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40, 3 (Mar. 1997), 77--87.

Digital Library

Google Scholar

[8]

]]Pazzani, M. J., & Billsus D. 2007. Content-based recommendation systems. The Adaptive Web, 325--341.

Digital Library

Google Scholar

[9]

]]Resnick, P. and Varian, H. R. 1997. Recommender systems. Commun. ACM 40, 3 (Mar. 1997), 56--58.

Digital Library

Google Scholar

[10]

]]Schafer, J. B., Konstan, J., and Riedi, J. 1999. Recommender systems in e-commerce. Proc. EC'99, 158--166.

Digital Library

Google Scholar

Cited By

View all

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
Show More Cited By

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

Recommendations

Improving Collaborative Filtering Based Recommenders Using Topic Modelling
WI-IAT '14: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01

Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating ...
Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...

Comments

Information & Contributors

Information

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]

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

RecSys '10

Sponsor:

SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
1,178
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
https://doi.org/10.1016/j.jksuci.2018.02.015
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
https://doi.org/10.1007/978-3-319-90092-6_15
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
https://dl.acm.org/doi/10.1016/j.jvcir.2015.02.007
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
https://dl.acm.org/doi/10.1007/s10462-012-9359-6
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
https://dl.acm.org/doi/10.1145/2556270
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

Supplementary Material

References

Cited By

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

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations