Article

Insert movie reference here: a system to bridge conversation and item-oriented web sites

Authors:

Loren TerveenAuthors Info & Claims

CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 951 - 954

https://doi.org/10.1145/1124772.1124914

Published: 22 April 2006 Publication History

Get Access

Abstract

Item-oriented Web sites maintain repositories of information about things such as books, games, or products. Many of these Web sites offer discussion forums. However, these forums are often disconnected from the rich data available in the item repositories. We describe a system, movie linking, that bridges a movie recommendation Web site and a movie-oriented discussion forum. Through automatic detection and an interactive component, the system recognizes references to movies in the forum and adds recommendation data to the forums and conversation threads to movie pages. An eight week observational study shows that the system was able to identify movie references with precision of .93 and recall of .78. Though users reported that the feature was useful, their behavior indicates that the feature was more successful at enriching the interface than at integrating the system.

References

[1]

Google suggest. http://labs.google.com/suggestfaq.html

Google Scholar

[2]

P. J. Ludford, D. Cosley, D. Frankowski, and L. Terveen. Think different: increasing online community participation using uniqueness and group dissimilarity. In CHI '04, pages 631-638, New York, NY, USA, 2004. ACM Press.

Digital Library

Google Scholar

[3]

mvnforum. http://www.mvnforum.com

Google Scholar

[4]

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative Filtering recommendation algorithms. In WWW '01, pages 285-295, New York, NY, USA, 2001. ACM Press.

Digital Library

Google Scholar

[5]

T. Schoberth, J. Preece, and A. Heinzl. Online communities: A longitudinal analysis of communication activities. In HICSS, page 216, 2003.

Google Scholar

Cited By

View all

Jordan PAuernheimer B(2017)The Fiction in Computer Science: A Qualitative Data Analysis of the ACM Digital Library for Traces of Star TrekAdvances in Usability and User Experience10.1007/978-3-319-60492-3_48(508-520)Online publication date: 24-Jun-2017
https://doi.org/10.1007/978-3-319-60492-3_48
Harper FKonstan J(2015)The MovieLens DatasetsACM Transactions on Interactive Intelligent Systems10.1145/28278725:4(1-19)Online publication date: 22-Dec-2015
https://dl.acm.org/doi/10.1145/2827872
Dillon TChen YChang EMohania M(2014)Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power SystemsProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-662-44654-6_1(1-19)Online publication date: 2014
https://doi.org/10.1007/978-3-662-44654-6_1
Show More Cited By

Index Terms

Insert movie reference here: a system to bridge conversation and item-oriented web sites
1. Human-centered computing

Recommendations

Movie SCoRe: Personalized Movie Recommendation on Mobile Devices
PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer behaviour. In this paper, we present Movie SCoRe, a mobile device application for personalized movie recommendation, based on a ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Movie recommendation using YouTube movie trailer data as the side information
ASONAM '20: Proceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

The user feedback data such as likes, dislikes, comments on movie trailers posted on YouTube can be a useful information source for movie recommender systems. In this paper, we study the effect of adding the feedback data on trailers as a type of the ...

Comments

Information & Contributors

Information

Published In

CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2006

1353 pages

ISBN:1595933727

DOI:10.1145/1124772

Editors:
Rebecca Grinter
Georgia Institute of Technology, USA
,
Thomas Rodden
University of Nottingham, UK
,
Paul Aoki
Intel, USA
,
Ed Cutrell
Microsoft, USA
,
Robin Jeffries
Google, USA
,
Gary Olson
University of Michigan, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

CHI06

Sponsor:

CHI06: CHI 2006 Conference on Human Factors in Computing Systems

April 22 - 27, 2006

Québec, Montréal, Canada

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
774
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Jordan PAuernheimer B(2017)The Fiction in Computer Science: A Qualitative Data Analysis of the ACM Digital Library for Traces of Star TrekAdvances in Usability and User Experience10.1007/978-3-319-60492-3_48(508-520)Online publication date: 24-Jun-2017
https://doi.org/10.1007/978-3-319-60492-3_48
Harper FKonstan J(2015)The MovieLens DatasetsACM Transactions on Interactive Intelligent Systems10.1145/28278725:4(1-19)Online publication date: 22-Dec-2015
https://dl.acm.org/doi/10.1145/2827872
Dillon TChen YChang EMohania M(2014)Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power SystemsProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-662-44654-6_1(1-19)Online publication date: 2014
https://doi.org/10.1007/978-3-662-44654-6_1
Peiris KGallupe R(2012)A Conceptual Framework for Evolving, Recommender Online Learning SystemsDecision Sciences Journal of Innovative Education10.1111/j.1540-4609.2012.00347.x10:3(389-412)Online publication date: 4-Jul-2012
https://doi.org/10.1111/j.1540-4609.2012.00347.x
Dong XHarper FKonstan JTan DFitzpatrick GGutwin CBegole BKellogg W(2011)Entity-linking interfaces in user-contributed contentProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/1978942.1979261(2187-2196)Online publication date: 7-May-2011
https://dl.acm.org/doi/10.1145/1978942.1979261
Zhou DResnick PBergman LTuzhilin ABurke RFelfernig ASchmidt-Thieme L(2009)Assessment of conversation co-mentions as a resource for software module recommendationProceedings of the third ACM conference on Recommender systems10.1145/1639714.1639737(133-140)Online publication date: 23-Oct-2009
https://dl.acm.org/doi/10.1145/1639714.1639737
Song MNamgoong HKim HEune J(2009)A Proposed Movie Recommendation Method Using Emotional Word SelectionProceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 200910.1007/978-3-642-02774-1_57(525-534)Online publication date: 15-Jul-2009
https://dl.acm.org/doi/10.1007/978-3-642-02774-1_57
Milne DWitten IShanahan JAmer-Yahia SManolescu IZhang YEvans DKolcz AChoi KChowdury A(2008)Learning to link with wikipediaProceedings of the 17th ACM conference on Information and knowledge management10.1145/1458082.1458150(509-518)Online publication date: 26-Oct-2008
https://dl.acm.org/doi/10.1145/1458082.1458150
Drenner SSen STerveen LPu PBridge DMobasher BRicci F(2008)Crafting the initial user experience to achieve community goalsProceedings of the 2008 ACM conference on Recommender systems10.1145/1454008.1454039(187-194)Online publication date: 23-Oct-2008
https://dl.acm.org/doi/10.1145/1454008.1454039
Zhou DOostendorp NHess MResni k PCzerwinski MLund ATan D(2008)Conversation pivots and double pivotsProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/1357054.1357209(1009-1012)Online publication date: 6-Apr-2008
https://dl.acm.org/doi/10.1145/1357054.1357209
Show More Cited By

View Options

Get Access

Login options

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

Cited By

Index Terms

Recommendations

Movie SCoRe: Personalized Movie Recommendation on Mobile Devices

Improving Accuracy of Recommender System by Item Clustering

Movie recommendation using YouTube movie trailer data as the side information