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
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Forecasting with Exponential Smoothing
Hyndman, R. J., Koehler, A. B., Ord, K., & Snyder, R. D. (2008) · Springer series in statistics · doi:10.1007/978-3-540-71918-2 · Cited by 1,630
Collaborative filtering with temporal dynamics
Koren, Y. (2009) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557072 · Cited by 1,217
Bursty and hierarchical structure in streams
Kleinberg, J. M. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775061 · Cited by 1,182
Patterns of temporal variation in online media
Yang, J., & Leskovec, J. (2011) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1935826.1935863 · Cited by 984
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2754 · Cited by 726
The cepstrum: A guide to processing
Childers, D. G., Skinner, D. P., & Kemerait, R. C. (1977) · Proceedings of the IEEE · doi:10.1109/proc.1977.10747 · Cited by 600
Gabel, G. O. (2017) · Lexikon des gesamten Buchwesens Online · doi:10.1163/9789004337862_lgbo_com_150392 · Cited by 503
Radinsky, K., Agichtein, E., Gabrilovich, E., & Markovitch, S. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963455 · Cited by 405
Self-citationHourly analysis of a very large topically categorized web query log
Beitzel, S. M., Jensen, E. C., Chowdhury, A., Grossman, D. A., & Frieder, O. (2004) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1008992.1009048 · Cited by 304
Research works printed but not linked: 1
- J. Kleinberg . Temporal dynamics of on-line information systems. Data Stream Management: Processing High-Speed Data Streams . Springer , 2006 . J. Kleinberg. Temporal dynamics of on-line information systems. Data Stream Management: Processing High-Speed Data Streams. Springer, 2006.
Cited by
Sorted by citations
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“For instance, 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 (Radinsky et al., 2012).”
BackgroundA survey on predicting the popularity of web content
Tatar, A., de Amorim, M. D., Serge Fdida, B., & Antoniadis, P. (2014) · Journal of Internet Services and Applications · doi:10.1186/s13174-014-0008-y · Cited by 250
Hogg, T., & Lerman, K. (2012) · EPJ Data Science · doi:10.1140/epjds5 · Cited by 151
Time-sensitive query auto-completion
Shokouhi, M., & Radinsky, K. (2012) · Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2348283.2348364 · Cited by 95
Self-citationExploring Interpretable LSTM Neural Networks over Multi-Variable DataPreprint
Guo, T., Lin, T., & Antulov-Fantulin, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12034 · Cited by 154
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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