Article

Combining eye movements and collaborative filtering for proactive information retrieval

Authors:

Kai Puolamäki,

Jarkko Salojärvi,

Samuel KaskiAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 146 - 153

https://doi.org/10.1145/1076034.1076062

Published: 15 August 2005 Publication History

Abstract

We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Implicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from existing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a state-of-the-art probabilistic latent variable model, computed using Markov Chain Monte Carlo techniques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their combination was significantly better than by chance. The best prediction accuracy still leaves room for improvement but shows that proactive information retrieval and combination of many sources of relevance feedback is feasible.

References

[1]

J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proceedings of ICML'04, Twenty-first International Conference on Machine Learning. ACM Press, New York, 2004.

Digital Library

[2]

D. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.

Digital Library

[3]

W. Buntine. Variational extensions to EM and multinomial PCA. In T. Elomaa, H. Mannila, and H. Toivonen, editors,Proceedings of ECML'02, 13th European Conference on Machine Learning, pages 23--34. Springer, Berlin, 2002.

Digital Library

[4]

T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22:89--115, 2004.

Digital Library

[5]

A. Hyrskykari, P. Majaranta, and K.-J. Räihä. Proactive response to eye movements. In G. W. M. Rauterberg, M. Menozzi, and J. Wesson, editors, INTERACT'03, pages 129--136. IOS Press, 2003.

[6]

http://www.pascal-network.org/Challenges/IREM/.

[7]

D. Kelly and J. Teevan. Implicit feedback for inferring user preference: a bibliography. SIGIR Forum, 37(2):18--28, 2003.

Digital Library

[8]

J. Konstan, B. Miller, D. Maltz, and J. Herlocker. Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77--87, 1997.

Digital Library

[9]

C. L. Lisetti and F. Nasoz. Maui: a multimodal affective user interface. In MULTIMEDIA'02: Proceedings of the tenth ACM international conference on Multimedia, pages 161--170. ACM Press, 2002.

Digital Library

[10]

P. P. Maglio, R. Barrett, C. S. Campbell, and T. Selker. Suitor: an attentive information system. In Proceedings of the 5th International Conference on Intelligent User Interfaces, pages 169--176. ACM Press, 2000.

Digital Library

[11]

P. P. Maglio and C. S. Campbell. Attentive agents. Communications of the ACM, 46(3):47--51, 2003.

Digital Library

[12]

B. Marlin. Modeling user rating profiles for collaborative filtering. In Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.

Digital Library

[13]

T. Partala, V. Surakka, and T. Vanhala. Person-independent estimation of emotional experiences from facial expressions. In IUI'05: Proceedings of the 10th International Conference on Intelligent User Interfaces, pages 246--248. ACM Press, 2005.

Digital Library

[14]

A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of UAI-2001, pages 437--444. Morgan Kaufmann, 2001.

Digital Library

[15]

D. Povey, P. Woodland, and M. Gales. Discriminative map for acoustic model adaptation. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP'03), volume 1, pages 312--315, 2003.

[16]

J. K. Pritchard, M. Stephens, and P. Donnelly. Inference of population structure using multilocus genotype data. Genetics, 155:945--59, 2000.

[17]

L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77:257--286, 1989.

[18]

K. Rayner. Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124:372--422, 1998.

[19]

J. Salojärvi, I. Kojo, J. Simola, and S. Kaski. Can relevance be inferred from eye movements in information retrieval? In Proceedings of WSOM'03, Workshop on Self-Organizing Maps, pages 261--266. Kyushu Institute of Technology, Kitakyushu, Japan, 2003.

[20]

J. Salojärvi, K. Puolamäki, and S. Kaski. Relevance feedback from eye movements for proactive information retrieval. In J. Heikkilä, M. Pietikäinen, and O. Silvén, editors, Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004), Oulu, Finland, 2004.

[21]

D. D. Salvucci and J. R. Anderson. Automated eye-movement protocol analysis. Human-Computer Interaction, 16:39--86, 2001.

Digital Library

[22]

U. Shardanand and P. Maes. Social information filtering: Algorithms for automating `word of mouth'. In Proceedings of Computer Human Interaction, pages 210--217, 1995.

Digital Library

[23]

I. Starker and R. A. Bolt. A gaze-responsive self-disclosing display. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3--10. ACM Press, 1990.

Digital Library

[24]

A. Viterbi. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13:260--269, 1967.

Digital Library

[25]

D. J. Ward and D. J. MacKay. Fast hands-free writing by gaze direction. Nature, 418:838, 2002.

Cited By

Brown TLiu JAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)A reference dependence approach to enhancing early prediction of session behavior and satisfactionProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533294(1-5)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3533294
Lukander KToivanen MPuolamäki K(2020)Inferring Intent and Action from Gaze in Naturalistic BehaviorCognitive Analytics10.4018/978-1-7998-2460-2.ch074(1464-1482)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch074
Davari MHienert DKern DDietze SO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)The Role of Word-Eye-Fixations for Query Term PredictionProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378010(422-426)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378010
Show More Cited By

Index Terms

Combining eye movements and collaborative filtering for proactive information retrieval

Recommendations

Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Typicality-Based Collaborative Filtering Recommendation

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 ACM.

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]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGIR05

Sponsor:

SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
1,110
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)3

Reflects downloads up to 01 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Brown TLiu JAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)A reference dependence approach to enhancing early prediction of session behavior and satisfactionProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533294(1-5)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3533294
Lukander KToivanen MPuolamäki K(2020)Inferring Intent and Action from Gaze in Naturalistic BehaviorCognitive Analytics10.4018/978-1-7998-2460-2.ch074(1464-1482)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch074
Davari MHienert DKern DDietze SO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)The Role of Word-Eye-Fixations for Query Term PredictionProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378010(422-426)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378010
Bottos SBalasingam B(2020)Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov ModelsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2020.298352569:10(7857-7868)Online publication date: Oct-2020
https://doi.org/10.1109/TIM.2020.2983525
Mallampalli KPatel SIyengar RSridharan KRaghavan M(2020)Tool for image annotation based on gaze2020 International Conference on Signal Processing and Communications (SPCOM)10.1109/SPCOM50965.2020.9179496(1-5)Online publication date: Jul-2020
https://doi.org/10.1109/SPCOM50965.2020.9179496
Bottos SBalasingam B(2019)Tracking the Progression of Reading Through Eye-gaze Measurements2019 22th International Conference on Information Fusion (FUSION)10.23919/FUSION43075.2019.9011436(1-8)Online publication date: Jul-2019
https://doi.org/10.23919/FUSION43075.2019.9011436
Jacucci GBarral ODaee PWenzel MSerim BRuotsalo TPluchino PFreeman JGamberini LKaski SBlankertz B(2019)Integrating neurophysiologic relevance feedback in intent modeling for information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2416170:9(917-930)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1002/asi.24161
Sen PGanguly DJones GCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Procrastination is the Thief of TimeThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210114(1157-1160)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210114
Lukander KToivanen MPuolamäki K(2017)Inferring Intent and Action from Gaze in Naturalistic BehaviorInternational Journal of Mobile Human Computer Interaction10.5555/3213394.32133989:4(41-57)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.5555/3213394.3213398
Vrochidis SPatras IKompatsiaris I(2017)Gaze movement-driven random forests for query clustering in automatic video annotationMultimedia Tools and Applications10.5555/3048787.304883776:2(2861-2889)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3048787.3048837
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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten