The Effect of Ad Blocking on User Engagement with the Web

Miroglio, Ben Paul; Zeber, David; Kaye, Jofish; Weiss, Rebecca

doi:10.1145/3178876.3186162

Cited by 36 publications

(13 citation statements)

References 3 publications

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“…Firefox has revealed that two groups were tested: one using adblocks, the other one not using them. For the first group, there were increases in both active time spent in the browser and the number of pages viewed, while seeing no change in the number of searches [40].…”

Section: Other Outcomes By Using Adblock Softwarementioning

confidence: 89%

Evaluation of Adblock Software Usage

Sołtysik-Piorunkiewicz

Strzelecki

Abramek

2019

J. Complex Syst. Inform. Model. Q.

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The article shows the main factors of adblocking software usage. The study was based on data obtained by a web questionnaire. The research was focused on evaluation of ad blocking (adblock) software usage factors in five categories: (1) gender, age, and education; (2) use of advertising and sources of knowledge about advertising; (3) technical and social reasons for blocking online advertisements; (4) usage of an adblock-wall; and (5) type of online advertisement. An evaluation of adblock usage factors revealed four main technical reasons for adblock usage connected with website technology and web development problemsinterruption, amount of ads, speed, and security; and one social reason for adblock usage, namely, the problem of privacy.

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Section: Other Outcomes By Using Adblock Softwarementioning

confidence: 89%

Evaluation of Adblock Software Usage

Sołtysik-Piorunkiewicz

Strzelecki

Abramek

2019

J. Complex Syst. Inform. Model. Q.

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“…One conclusion that can be drawn from the literature is that not all page views (PVs) are equal. Some PVs are highly engaging, others are less engaging, and some may even disengage the reader [10,11]. Identifying which reading behaviors are highly engaging and disengaging is important to retain subscribers and to understand the drivers of churn.…”

Section: Discovering Hidden Group Structure: News Engagement Examplementioning

confidence: 99%

Coefficient tree regression for generalized linear models

Sürer

Apley

Malthouse

2021

Statistical Analysis

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Large regression data sets are now commonplace, with so many predictors that they cannot or should not all be included individually. In practice, derived predictors are relevant as meaningful features or, at the very least, as a form of regularized approximation of the true coefficients. We consider derived predictors that are the sum of some groups of individual predictors, which is equivalent to predictors within a group sharing the same coefficient. However, the groups of predictors are usually not known in advance and must be discovered from the data. In this paper we develop a coefficient tree regression algorithm for generalized linear models to discover the group structure from the data. The approach results in simple and highly interpretable models, and we demonstrated with real examples that it can provide a clear and concise interpretation of the data. Via simulation studies under different scenarios we showed that our approach performs better than existing competitors in terms of computing time and predictive accuracy.

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“…Some of the most popular classes of extensions for the Firefox browser are ad blockers, password managers, and download helpers [36]. As well as benefiting users directly through expanded functionality or convenience, the extension ecosystem provides indirect value to Mozilla in the form of increased user engagement [26] and decreased churn associated with browser customization.…”

Section: Introductionmentioning

confidence: 99%

Telemetry-Aware Add-on Recommendation for Web Browser Customization

Lopatka¹,

Ng²,

Miroglio³

et al. 2019

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

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Web Extensions (add-ons) allow clients to customize their Web browsing experience through the addition of auxiliary features to their browsers. The add-on ecosystem is a market differentiator for the Firefox browser, offering contributions from both commercial entities and community developers. In this paper, we present the Telemetry-Aware Add-on Recommender (TAAR), a system for recommending add-ons to Firefox users by leveraging separate models trained to three main sources of user data: the set of add-ons a user already has installed; usage and interaction data (browser Telemetry); and the language setting of the user's browser (locale). We build individual recommendation models for each of these data sources, and combine the recommendations they generate using a linear stacking ensemble method. Our method employs a novel penalty function for tuning weight parameters, which is adapted from the log likelihood ratio cost function, allowing us to scale the penalty of both correct and incorrect recommendations using the confidence weights associated with the individual component model recommendations. This modular approach provides a way to offer relevant personalized recommendations while respecting Firefox's granular privacy preferences and adhering to Mozilla's lean data collection policy. To evaluate our recommender system, we ran a large-scale randomized experiment that was deployed to 350,000 Firefox users and localized to 11 languages. We found that, overall, users were 4.4% more likely to install add-ons recommended by our ensemble method compared to a curated list. Furthermore, the magnitude of the increase varies significantly across locales, achieving over 8% improvement among German-language users. CCS CONCEPTS • Information systems → Personalization; Web applications; Recommender systems; • Human-centered computing → Webbased interaction.

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The Effect of Ad Blocking on User Engagement with the Web

Cited by 36 publications

References 3 publications

Evaluation of Adblock Software Usage

Evaluation of Adblock Software Usage

Coefficient tree regression for generalized linear models

Telemetry-Aware Add-on Recommendation for Web Browser Customization

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