short-paper

Analyzing and modeling user curiosity in online content consumption: a LastFM case study

Authors:

Alexandre M. Sousa,

Jussara M. Almeida,

Flavio FigueiredoAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 426 - 431

https://doi.org/10.1145/3341161.3342917

Published: 15 January 2020 Publication History

Abstract

Curiosity is a natural trait of human behavior. When we take into account the time we spend consuming content online, it is expected that at least a fraction of that time was driven by curious behavior. Aiming at understanding how curiosity drives online information consumption, we here propose a model that captures user curiosity relying on several stimulus metrics. Our model relies on the well-established Wundt's curve from psychology and is based on metrics capturing Novelty, Complexity and Uncertainty as key stimuli driving one's curiosity. As a case study, we apply our model on a dataset of online music consumption from LastFM. We found that there are four main types of user behaviors in terms of how the curiosity stimulus metrics drive the user accesses to online music. These are characterized based on the diversity in the songs, artists and musical genres accessed.

References

[1]

P. Singer, "Modeling aspects of human trails on the web by philipp singer, with prateek jain as coordinator," SIGWEB, no. Winter, 2016.

[2]

W. Youyou, M. Kosinski, and D. Stillwell, "Computer-based personality judgments are more accurate than those made by humans," National Academy of Sciences, vol. 112, no. 4, pp. 1036--1040, 2015.

[3]

A. M. Santos, "A hybrid recommendation system based on human curiosity," in Proceedings of the 9th ACM RecSys, 2015.

[4]

P. Zhao and D. L. Lee, "How much novelty is relevant?: It depends on your curiosity," in Proceedings of the 39th SIGIR ACM, 2016.

[5]

A. Greasley, A. Lamont, and J. Sloboda, "Exploring musical preferences: An in-depth qualitative study of adults' liking for music in their personal collections," Qualitative Research in Psychology, vol. 10, no. 4, 2013.

[6]

D. Berlyne, Conflict, Arousal and Curiosity, ser. McGraw-Hill series in psychology. NY, USA: McGraw-Hill, 1960.

[7]

G. Loewenstein, "The psychology of curiosity: A review and reinterpretation," Psychological Bulletin, vol. 116, no. 1, pp. 75--98, 1994.

[8]

Q. Wu and C. Miao, "Curiosity: From psychology to computation," ACM Computing Surveys, vol. 46, no. 2, pp. 18:1--18:26, 2013.

Digital Library

[9]

K. E. Merrick and M. L. Maher, Motivated Reinforcement Learning: Curious Characteres for Multiuser Gamers. Germany: Springer-Verlag Berlin Heidelberg, 2009.

[10]

E. Law and et al, "Curiosity killed the cat, but makes crowdwork better," in Proceedings of the ACM CHI, 2016.

[11]

A. Manyangara and E. G. Toms, "The effect of cognitive style and curiosity on information task multitasking," in ACM IIiX, 2010.

[12]

R. Saunders and J. S. Gero, "How to study artificial creativity," in Proceedings of the 4th ACM Creativity & Cognition, 2002.

[13]

L. Macedo and A. Cardoso, "The role of surprise, curiosity and hunger on exploration of unknown environments populated with entities," in IEEE EPIA, 2005.

[14]

V. Maccatrozzo and et al, "Sirup: Serendipity in recommendations via user perceptions," in Proceedings of the 22nd ACM IUI, 2017.

[15]

X. Niu and F. Abbas, "A framework for computational serendipity," in Proceedings of the 25th UMAP ACM, 2017.

[16]

Q. Wu, S. Liu, and C. Miao, "Modeling uncertainty driven curiosity for social recommendation," in Proceedings of the ACM WI, 2017.

[17]

D. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge University Press, 2005.

[18]

M. Schedl, "The lfm-1b dataset for music retrieval and recommendation," in Proceedings of the ACM ICMR, 2016.

[19]

M. Schedl and B. Ferwerda, "Large-scale analysis of group-specific music genre taste from collaborative tags," in IEEE ISM, 2017.

[20]

D. Sculley, "Web-scale k-means clustering," in Proccedings of the ACM WWW, 2010.

[21]

D. A. Menasce and V. Almeida, Scaling for E Business: Technologies, Models, Performance, and Capacity Planning. USA: Pren. Hall, 2000.

[22]

A. M. Ramos, N. Andrade, and L. B. Marinho, "Exploring the relation between novelty aspects and preferences in music listening," in Proceedings of the 14th ISMIR, 2013.

[23]

A. R. Benson, R. Kumar, and A. Tomkins, "Modeling user consumption sequences," in Proceedings of the 25th ACM WWW, 2016.

[24]

C. Schröder and S. Rahmann, "A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification," Algorithms for Molecular Biology, vol. 12, no. 1, pp. 12--21, 2017.

Cited By

Du JXu KGao Q(2024)P3C: A Curiosity-Driven Recommendation Method Based on Preference and ConsensusProceedings of the 2024 6th International Conference on Big Data Engineering10.1145/3688574.3688589(103-110)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3688574.3688589
Sousa AAlmeida JFigueiredo F(2022)Metrics of social curiosity: The WhatsApp caseOnline Social Networks and Media10.1016/j.osnem.2022.10020029(100200)Online publication date: May-2022
https://doi.org/10.1016/j.osnem.2022.100200

Analyzing and modeling user curiosity in online content consumption: a LastFM case study
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

Recommendations

Designing for Curiosity: An Interdisciplinary Workshop
CHI EA '17: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Curiosity is a ubiquitous characteristic of humans and a central mechanism for motivating learning, information seeking behaviors, and sustained engagements with everyday artifacts, such as artistic creations, commercial products, interactive displays ...
Curiosity Killed the Cat, but Makes Crowdwork Better
CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Crowdsourcing systems are designed to elicit help from humans to accomplish tasks that are still difficult for computers. How to motivate workers to stay longer and/or perform better in crowdsourcing systems is a critical question for designers. ...
Analysing user generated content related to art history
i-KNOW '11: Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies

For the canon of important works of art, lots of information is available on the Internet. Different Web platforms exist that show artworks with additional information and an Art historical description. While this information is suitable for experts in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 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

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

CAPES
Fapemig
CNPq

Conference

ASONAM '19

Sponsor:

SIGKDD

ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

Acceptance Rates

ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
162
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)3

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Du JXu KGao Q(2024)P3C: A Curiosity-Driven Recommendation Method Based on Preference and ConsensusProceedings of the 2024 6th International Conference on Big Data Engineering10.1145/3688574.3688589(103-110)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3688574.3688589
Sousa AAlmeida JFigueiredo F(2022)Metrics of social curiosity: The WhatsApp caseOnline Social Networks and Media10.1016/j.osnem.2022.10020029(100200)Online publication date: May-2022
https://doi.org/10.1016/j.osnem.2022.100200

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.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents