The DOI matches its Crossref record; 22 of the 25 references it cites were found in a registry or an index.
Modeling and predicting behavioral dynamics on the web
Abstract
User behavior on the Web changes over time. For example, the queries that people issue to search engines, and the underlying informational goals behind the queries vary over time. In this paper, we examine how to model and predict this temporal user behavior. We develop a temporal modeling framework adapted from physics and signal processing that can be used to predict time-varying user behavior using smoothing and trends. We also explore other dynamics of Web behaviors, such as the detection of periodicities and surprises. We develop a learning procedure that can be used to construct models of users' activities based on features of current and historical behaviors. The results of experiments indicate that by using our framework to predict user behavior, we can achieve significant improvements in prediction compared to baseline models that weight historical evidence the same for all queries. We also develop a novel learning algorithm that explicitly learns when to apply a given prediction model among a set of such models. Our improved temporal modeling of user behavior can be used to enhance query suggestions, crawling policies, and result ranking.
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In the citing paper
“there are variations on when and for how long users focus on certain topics that may be triggered by current trends, seasonality or periodicity in activities, or even noise”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Radinsky et al., 2012
In the citing paper
“Seasonal and periodic phenomena. These can trigger systematic variations in usage patterns”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Radinsky et al., 2012
Citations
Cites
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Koren, Y. (2009) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557072 · Cited by 1,217
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Yang, J., & Leskovec, J. (2011) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1935826.1935863 · Cited by 984
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Self-citationHourly analysis of a very large topically categorized web query log
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Research works printed but not linked: 1
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Yousaf, J., Li, J., & Ma, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53914-5_6 · Cited by 1
Generating realistic workload for web performance studies
Peña-Ortiz, R., Gil, J. A., Sahuquillo, J., Pont, A., & Domènech, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800887-4.00006-7 · Cited by 1
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Self-citationDrutsa, A. V., Gusev, G., & Serdyukov, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.09083 · Cited by 1
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User behavior analysis based on decomposition of time-stamp sequence
Huijun, C., Hong, S., & Yu-ming, Y. (2013) · Wireless and Optical Communications Conference · doi:10.1109/wocc.2013.6676413
Hendahewa, C. (2014) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2556195.2556203
Neighborhood User Estimation fromWeb Access Log in EC Service
Koketsu, T., Yanagimoto, H., & Yoshioka, M. (2013) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai.2013.67
MINING WEB DYNAMICS FOR SEARCH
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Koketsu, T., Yanagimoto, H., & Yoshioka, M. (2013) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.133.1983
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You, J., Yu, Z., Gao, L., Zhang, G., Xue, H., & Wang, J. (2017) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2017.84
Prediction of user behaviour on the Web
Burlutskiy, N. (2017)
You, J., Xue, H., Yu, Z., Zhang, X., & Wang, J. (2017) · IEICE Transactions on Communications · doi:10.1587/transcom.2017ebp3219
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Yi, J., & Chang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07553
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Liang, Y., Zhou, H., Li, T., & Shen, H. (2025) · Applied Sciences · doi:10.3390/app152010967
Uncovering periodic group behaviors in temporal bipartite social graphs
Yang, B., Tang, D., Bao, Q., Xu, J., Tian, H., & Jiang, M. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x251401352
Machine learning algorithms for behavior prediction in cloud computing architectures
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Metadata
- Authors
- Radinsky, K., Svore, K., Dumais, S., Teevan, J., Bocharov, A., & Horvitz, E.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 21st international conference on World Wide Web
- Publisher
- ACM
- Published
- 2012-04-16
- Pages
- 599-608
- OpenAlex
- W2012354735
- Cited by
- 122
- References
- 25
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 10 · 2018: 7 · 2017: 9 · 2016: 12 · 2015: 19 · 2014: 15 · 2013: 22 · 2012: 9
- FWCI
- 16.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- temporal user behavior, search queries, temporal modeling, periodicity detection, surprise detection, query suggestion, result ranking
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