research-article

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Effects of Past Interactions on User Experience with Recommended Documents

Authors:

Farnaz Jahanbakhsh,

Ahmed Hassan Awadallah,

Susan T. Dumais,

Xuhai XuAuthors Info & Claims

CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

Pages 153 - 162

https://doi.org/10.1145/3343413.3377977

Published: 14 March 2020 Publication History

Abstract

Recommender systems are commonly used in entertainment, news, e-commerce, and social media. Document recommendation is a new and under-explored application area, in which both re-finding and discovery of documents need to be supported. In this paper we provide an initial exploration of users' experience with recommended documents, with a focus on how prior interactions influence recognition and interest. Through a field study of more than 100 users, we investigate the effects of past interactions with recommended documents on users' recognition of, prior intent to open, and interest in the documents. We examined different presentations of interaction history, and the recency and richness of prior interaction. We found that presentation only influenced recognition time. Our findings also indicate that people are more likely to recognize documents they had accessed recently and to do so more quickly. Similarly, documents that people had interacted with more deeply were also more frequently and quickly recognized. However, people were more interested in older documents or those with which they had less involved interactions. This finding suggests that in addition to helping users quickly access documents they intend to re-find, document recommendation can add value in helping users discover other documents. Our results offer implications for designing document recommendation systems that help users fulfil different needs.

References

[1]

Tarfah Alrashed, Ahmed Hassan Awadallah, and Susan Dumais. 2018. The Lifetime of Email Messages: A Large-Scale Analysis of Email Revisitation. In Proc. CHIIR.

Digital Library

[2]

Ashton Anderson, Ravi Kumar, Andrew Tomkins, and Sergei Vassilvitskii. 2014. The dynamics of repeat consumption. In Proceedings of the 23rd international conference on World wide web. ACM, 419--430.

Digital Library

[3]

Austin R Benson, Ravi Kumar, and Andrew Tomkins. 2016. Modeling user consumption sequences. In Proceedings of the 25th International Conference on World WideWeb. InternationalWorld WideWeb Conferences Steering Committee, 519--529.

Digital Library

[4]

Ofer Bergman, Ruth Beyth-Marom, Rafi Nachmias, Noa Gradovitch, and Steve Whittaker. 2008. Improved search engines and navigation preference in personal information management. ACM Transactions on Information Systems (TOIS) 26, 4 (2008), 20.

Digital Library

[5]

Ofer Bergman, Steve Whittaker, Mark Sanderson, Rafi Nachmias, and Anand Ramamoorthy. 2010. The effect of folder structure on personal file navigation. Journal of the American Society for Information Science and Technology 61, 12 (2010), 2426--2441. https://doi.org/10.1002/asi.21415 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/asi.21415

Digital Library

[6]

Hugo L Borges and Ana C Lorena. 2010. A survey on recommender systems for news data. In Smart Information and Knowledge Management. Springer, 129--151.

[7]

Lukas Brozovsky and Vaclav Petricek. 2007. Recommender system for online dating service. arXiv preprint cs/0703042 (2007).

[8]

Jun Chen, Chaokun Wang, and Jianmin Wang. 2015. Will you" reconsume" the near past? fast prediction on short-term reconsumption behaviors. In Twenty- Ninth AAAI Conference on Artificial Intelligence.

Digital Library

[9]

Andy Cockburn, Saul Greenberg, Bruce McKenzie, Michael Jasonsmith, and Shaun Kaasten. 1999. WebView: A graphical aid for revisiting Web pages. In Proceedings of the OZCHI, Vol. 99. 15--22.

[10]

Andy Cockburn and Bruce McKenzie. 2001. What do web users do? An empirical analysis of web use. International Journal of human-computer studies 54, 6 (2001), 903--922.

Digital Library

[11]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. ACM, 191--198.

Digital Library

[12]

Edward Cutrell, Susan T Dumais, and Jaime Teevan. 2006. Searching to eliminate personal information management. Commun. ACM 49, 1 (2006), 58--64.

Digital Library

[13]

Susan Dumais, Edward Cutrell, Jonathan Cadiz, Gavin Jancke, Raman Sarin, and Daniel Robbins. 2003. Stuff I've Seen: A System for Personal Information Retrieval and Re-Use. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 72--79. https://doi.org/10.1145/860435.860451

[14]

M. Grbovic, G. Halawi, Z. Karnin, and Y. Maarek. 2014. How many folders do you really need?: Classifying email into a handful of categories. In Proc. CIKM.

[15]

Ziyu Guan, Can Wang, Jiajun Bu, Chun Chen, Kun Yang, Deng Cai, and Xiaofei He. 2010. Document Recommendation in Social Tagging Services. In Proceedings of the 19th International Conference on World Wide Web (WWW '10). ACM, New York, NY, USA, 391--400. https://doi.org/10.1145/1772690.1772731

Digital Library

[16]

Ido Guy, Naama Zwerdling, David Carmel, Inbal Ronen, Erel Uziel, Sivan Yogev, and Shila Ofek-Koifman. 2009. Personalized recommendation of social software items based on social relations. In Proceedings of the third ACM conference on Recommender systems. ACM, 53--60.

Digital Library

[17]

William Jones, Susan Dumais, and Harry Bruce. 2002. Once found, what then? a study of "keeping" behaviors in the personal use of web information. Proceedings of the American Society for Information Science and Technology 39, 1 (2002), 391-- 402.

[18]

William Jones and Michele Tepper. 2008. RESOURCES-Reviews-Keeping Found Things Found: The Study and Practice of Personal Information. netWorker: The Craft of Network Computing 12, 1 (2008), 33.

[19]

SeoYoung Lee and Junho Choi. 2017. Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies 103 (2017), 95--105.

Digital Library

[20]

Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces. ACM, 31--40.

Digital Library

[21]

Joel Mackenzie, Kshitiz Gupta, Fang Qiao, Ahmed Hassan Awadallah, and Milad Shokouhi. 2019. Exploring User Behavior in Email Re-Finding Tasks. In The World Wide Web Conference (WWW '19). ACM, New York, NY, USA, 1245--1255. https://doi.org/10.1145/3308558.3313450

[22]

Jens-Erik Mai. 2016. Looking for information: A survey of research on information seeking, needs, and behavior. Emerald Group Publishing.

[23]

Xueming Qian, He Feng, Guoshuai Zhao, and Tao Mei. 2013. Personalized recommendation combining user interest and social circle. IEEE transactions on knowledge and data engineering 26, 7 (2013), 1763--1777.

[24]

Nelson Siu, Lee Iverson, and Anthony Tang. 2006. Going with the Flow: Email Awareness and Task Management. In Proc. CSCW. 441--450.

Digital Library

[25]

Brent Smith and Greg Linden. 2017. Two decades of recommender systems at Amazon. com. Ieee internet computing 21, 3 (2017), 12--18.

[26]

Sandeep Tata, Alexandrin Popescul, Marc Najork, Mike Colagrosso, Julian Gibbons, Alan Green, Alexandre Mah, Michael Smith, Divanshu Garg, Cayden Meyer, et al. 2017. Quick access: building a smart experience for Google drive. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1643--1651.

Digital Library

[27]

Linda Tauscher and Saul Greenberg. 1997. How people revisit web pages: empirical findings and implications for the design of history systems. International Journal of Human-Computer Studies 47, 1 (1997), 97--137.

Digital Library

[28]

Jaime Teevan. 2008. How people recall, recognize, and reuse search results. ACM Transactions on Information Systems (TOIS) 26, 4 (2008), 19.

Digital Library

[29]

Jaime Teevan, Eytan Adar, Rosie Jones, and Michael AS Potts. 2007. Information re-retrieval: repeat queries in Yahoo's logs. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 151--158.

Digital Library

[30]

Jaime Teevan, Edward Cutrell, Danyel Fisher, Steven M Drucker, Gonzalo Ramos, Paul André, and Chang Hu. 2009. Visual snippets: summarizing web pages for search and revisitation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2023--2032.

Digital Library

[31]

L. Cadiz J. Venolia, G. Dabbish and A. Gupta. 2001. Supporting Email Workflow. Vol. 2088.

[32]

Sergey Volokhin and Eugene Agichtein. 2018. Understanding music listening intents during daily activities with implications for contextual music recommendation. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval. ACM, 313--316.

Digital Library

[33]

Kangning Wei, Jinghua Huang, and Shaohong Fu. 2007. A survey of e-commerce recommender systems. In 2007 international conference on service systems and service management. IEEE, 1--5.

[34]

Bo Xiao and Izak Benbasat. 2007. E-commerce product recommendation agents: use, characteristics, and impact. MIS quarterly 31, 1 (2007), 137--209.

Digital Library

[35]

Lili Zhao, Zhongqi Lu, Sinno Jialin Pan, and Qiang Yang. 2016. Matrix factorization+ for movie recommendation. In In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16). 3945--3951.

Digital Library

Cited By

Xu XYu AJonker TTodi KLu FQian XEvangelista Belo JWang TLi MMun AWu TShen JZhang TKokhlikyan NWang FSorenson PKim SBenko H(2023)XAIR: A Framework of Explainable AI in Augmented RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581500(1-30)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581500
Zheng XGuan MJia XGuo LLuo Y(2023)A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317069110:3(1189-1198)Online publication date: Jun-2023
https://doi.org/10.1109/TCSS.2022.3170691
Jahanbakhsh FNouri ESim RWhite RFourney A(2022)Understanding Questions that Arise When Working with Business DocumentsProceedings of the ACM on Human-Computer Interaction10.1145/35557616:CSCW2(1-24)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555761
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Index Terms

Effects of Past Interactions on User Experience with Recommended Documents
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

March 2020

596 pages

ISBN:9781450368926

DOI:10.1145/3343413

General Chairs:
Heather O'Brien
University of British Columbia, Canada
,
Luanne Freund
University of British Columbia, Canada
,
Program Chairs:
Ioannis Arapakis
Telefonica I+D, Spain
,
Orland Hoeber
University of Regina, Canada
,
Irene Lopatovska
Pratt Institute, USA

Copyright © 2020 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]

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Published: 14 March 2020

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CHIIR '20

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CHIIR '20: Conference on Human Information Interaction and Retrieval

March 14 - 18, 2020

Vancouver BC, Canada

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Cited By

Xu XYu AJonker TTodi KLu FQian XEvangelista Belo JWang TLi MMun AWu TShen JZhang TKokhlikyan NWang FSorenson PKim SBenko H(2023)XAIR: A Framework of Explainable AI in Augmented RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581500(1-30)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581500
Zheng XGuan MJia XGuo LLuo Y(2023)A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317069110:3(1189-1198)Online publication date: Jun-2023
https://doi.org/10.1109/TCSS.2022.3170691
Jahanbakhsh FNouri ESim RWhite RFourney A(2022)Understanding Questions that Arise When Working with Business DocumentsProceedings of the ACM on Human-Computer Interaction10.1145/35557616:CSCW2(1-24)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555761
Brackenbury WChard KElmore AUr B(2022)Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud StorageProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology10.1145/3526113.3545704(1-11)Online publication date: 29-Oct-2022
https://dl.acm.org/doi/10.1145/3526113.3545704
Jiang TLiu J(2022)Reflection on future directions: a systematic review of reported limitations and solutions in interactive information retrieval user studiesAslib Journal of Information Management10.1108/AJIM-05-2022-0253Online publication date: 19-Dec-2022
https://doi.org/10.1108/AJIM-05-2022-0253
Brackenbury WMcNutt AChard KElmore AUr B(2021)KondoCloud: Improving Information Management in Cloud Storage via Recommendations Based on File SimilarityThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474736(69-83)Online publication date: 10-Oct-2021
https://dl.acm.org/doi/10.1145/3472749.3474736
Brackenbury WHarrison GChard KElmore AUr BDiaz FShah CSuel TCastells PJones RSakai T(2021)Files of a Feather Flock Together? Measuring and Modeling How Users Perceive File Similarity in Cloud StorageProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462845(787-797)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462845
Xu XHassan Awadallah AT. Dumais SOmar FPopp BRounthwaite RJahanbakhsh F(2020)Understanding User Behavior For Document RecommendationProceedings of The Web Conference 202010.1145/3366423.3380071(3012-3018)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380071

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