Probabilistic Visitor Stitching on Cross-Device Web Logs

@article{Kim2017ProbabilisticVS,
  title={Probabilistic Visitor Stitching on Cross-Device Web Logs},
  author={Sungchul Kim and Nikhil Kini and Jay Pujara and Eunyee Koh and Lise Getoor},
  journal={Proceedings of the 26th International Conference on World Wide Web},
  year={2017}
}
Personalization -- the customization of experiences, interfaces, and content to individual users -- has catalyzed user growth and engagement for many web services. A critical prerequisite to personalization is establishing user identity. However the variety of devices, including mobile phones, appliances, and smart watches, from which users access web services from both anonymous and logged-in sessions poses a significant obstacle to user identification. The resulting entity resolution task of… 

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References

SHOWING 1-10 OF 32 REFERENCES

Overcoming browser cookie churn with clustering

A novel method to cluster browser cookies into groups that are likely to belong to the same browser based on a statistical model of browser visitation patterns, and proposes a greedy heuristic algorithm for solving it.

Personalizing Search on Shared Devices

An oracle study is presented (with perfect knowledge of which searchers perform each action on each machine) to under-stand the effectiveness of ABP in predicting searchers' future interests, and a classifier is developed to determine when to apply it that yields sizable gains in personalization performance.

Effective personalization based on association rule discovery from web usage data

This paper proposes effective and scalable techniques for Web personalization based on association rule discovery from usage data that can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.

Peering Through the Shroud: The Effect of Edge Opacity on IP-Based Client Identification

A methodology is developed and implemented by which a server can make a more informed decision on whether to rely on IP addresses for client identification or to use more heavyweight forms of client authentication.

Probabilistic Deduplication of Anonymous Web Traffic

This paper solves the problem of identifying whether two cookies map to the same visitor by converting categorical variables like IP addresses, product search keywords and URLs with very high cardinalities to continuous numeric variables using the Jaccard coefficient for each attribute.

Cross-Device Search

This paper characterize multi-device search across four device types, including aspects of search behavior on each device (e.g., topics of interest) and characteristics of device transitions, and proposes models to predict aspects of cross- device search transitions.

Studying User Footprints in Different Online Social Networks

This paper presents the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks, and finds User ID and Name were found to be the most discriminative features for dis Ambiguating user profiles.

How Unique Is Your Web Browser?

  • P. Eckersley
  • Computer Science
    Privacy Enhancing Technologies
  • 2010
The degree to which modern web browsers are subject to "device fingerprinting" via the version and configuration information that they will transmit to websites upon request is investigated, and what countermeasures may be appropriate to prevent it is discussed.

Linking Users Across Domains with Location Data: Theory and Validation

This paper addresses the reconciliation problem for location-based datasets and introduces a robust method for this general setting, which outperforms naive rules and prior heuristics and can be shown to be robust even when data gets sparse.

People and Cookies: Imperfect Treatment Assignment in Online Experiments

It is shown that the estimated treatment effect in a cookie-level experiment converges to a weighted average of the marginal effects of treating more of a user's cookies, which underestimates the true person-level effect by a factor equal to the number of cookies per person.