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The increasing popularity of adblockers has prompted online publishers to retaliate against adblock users by deploying anti-adblock scripts, which detect adblock users and bar them from accessing content unless they disable their adblocker. To circumvent antiadblockers, adblockers rely on manually curated anti-adblock filter lists for removing anti-adblock scripts. Anti-adblock filter lists currently rely on informal crowdsourced feedback from users to add/remove filter list rules. In this paper, we present the first comprehensive study of anti-adblock filter lists to analyze their effectiveness against anti-adblockers. Specifically, we compare and contrast the evolution of two popular anti-adblock filter lists. We show that these filter lists are implemented very differently even though they currently have a comparable number of filter list rules. We then use the Internet Archive’s Wayback Machine to conduct a retrospective coverage analysis of these filter lists on Alexa top-5K websites over the span of last five years. We find that the coverage of these filter lists has considerably improved since 2014 and they detect anti-adblockers on about 9% of Alexa top-5K websites. To improve filter list coverage and speedup addition of new filter rules, we also design and implement a machine learning based method to automatically
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Top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons
Top-k lists are being increasingly utilized in various fields and applications including information retrieval, machine learning, and recommendation systems. Since multiple top-k lists may be generated by different algorithms to evaluate the same set of entities or system of interest, there is often a need to consolidate this collection of heterogeneous top-k lists to obtain a more robust and coherent list. This work introduces various exact mathematical formulations of the top-k list aggregation problem under the generalized Kendall tau distance. Furthermore, the strength of the proposed formulations is analyzed from a polyhedral point of view.
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- Award ID(s):
- 1850355
- PAR ID:
- 10336860
- Date Published:
- Journal Name:
- International Symposium on Combinatorial Optimization
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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