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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Variations on when and for how long users focus on certain topics may be triggered by current trends, seasonality or periodicity in activities, or even noise.
“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
Seasonal and periodic phenomena can trigger systematic variations in usage patterns.
“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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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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