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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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“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
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A Fusion Model Based on Dynamic Web Browsing Behavior Analysis for IoT Insider Threat Detection
Wang, J., Liu, J., Yan, T., Xia, M., Hong, J., & Zhou, C. (2022) · Sensors · doi:10.3390/s22176471 · Cited by 1
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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