On Web user tracking: How third-party http requests track users' browsing patterns for personalised advertising

Puglisi, Silvia; Rebollo-Monedero, David; Forné, Jordi

doi:10.1109/medhocnet.2016.7528432

Cited by 10 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is demonstrated in Fig. 2, influenced by Puglisi, Rebollo-Monedero and Forne (2016) study) [4]. This tracking is referred to as Third-party tracking since it is activated by URL domains that belong to the APs (e.g.…”

Section: Fig 1 Main Online Advertising Environment Entitiesmentioning

confidence: 92%

AdPExT: Designing a Tool to Assess Information Gleaned from Browsers by Online Advertising Platforms

Woensdregt¹,

Al-Khateeb

Epiphaniou

et al. 2019

2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3)

View full text Add to dashboard Cite

The world of online advertising is directly dependent on data collection of the online browsing habits of individuals to enable effective advertisement targeting and retargeting. However, these data collection practices can cause leakage of private data belonging to website visitors (endusers) without their knowledge. The growing privacy concern of end-users is amplified by a lack of trust and understanding of what and how advertisement trackers are collecting and using their data. This paper presents an investigation to restore the trust or validate the concerns. We aim to facilitate the assessment of the actual end-user related data being collected by advertising platforms (APs) by means of a critical discussion but also the development of a new tool, AdPExT (Advertising Parameter Extraction Tool), which can be used to extract third-party parameter key-value pairs at an individual key-value level. Furthermore, we conduct a survey covering mostly United Kingdom-based frequent internet users to gather the perceived sensitivity sentiment for various representative tracking parameters. End-users have a definite concern with regards to advertisement tracking of sensitive data by global dominating platforms such as Facebook and Google.

show abstract

Section: Fig 1 Main Online Advertising Environment Entitiesmentioning

confidence: 92%

AdPExT: Designing a Tool to Assess Information Gleaned from Browsers by Online Advertising Platforms

Woensdregt¹,

Al-Khateeb

Epiphaniou

et al. 2019

2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3)

View full text Add to dashboard Cite

show abstract

“…Fingerprinting (stateless tracking) has become an increasingly common practice used by advertisement enterprises (Sanchez-Rola et al, 2016). For companies that use web tracking, "efficient and successful advertising relies on predicting users' actions and tastes to a range of products to buy" (Puglisi et al, 2016). Interestingly, the existing tracking tools -both stateful and stateless -fail to address the complexity of buying decisions and, therefore, perform poorly at supporting desired behavior predictions (Melicher et al, 2016).…”

Section: Commercial Aspectsmentioning

confidence: 99%

Web Tracking - A Literature Review on the State of Research

Ermakova

Fabian

Bender

et al. 2018

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Web tracking seems to become ubiquitous in online business and leads to increased privacy concerns of users. This paper provides an overview over the current state of the art of web-tracking research, aiming to reveal the relevance and methodologies of this research area and creates a foundation for future work. In particular, this study addresses the following research questions: What methods are followed? What results have been achieved so far? What are potential future research areas? For these goals, a structured literature review based upon an established methodological framework is conducted. The identified articles are investigated with respect to the applied research methodologies and the aspects of web tracking they emphasize.

show abstract

“…Implicit feedback refers to implicit user behavior, such as browsing patterns, time spent browsing a particular product, clickstream data, etc. Websites typically collect such information using personalization services and third-party HTTP requests [8]. Explicit user feedback is more readily available and is of a high quality, examples being ratings and reviews by users.…”

Section: Literature Surveymentioning

confidence: 99%

QUAESTUS – A Top-N Recommender System with Ranking Matrix Factorization

Venkitaraman¹,

Mankad²,

Barbhaya³

2018

IJCA

View full text Add to dashboard Cite

The last decade has seen rapid strides being taken in the field of recommender systems, which has been driven by both consumer demand for personalization as well as academic interest in implementing more accurate and optimized versions of recommender systems. In this paper we have discussed our implementation of Quaestus, a top-n item-based collaborative filtering recommender system with ranked matrix factorization (for relevance based sorting) which we have tested on an e-commerce dataset. We have used sentiment analysis to understand the polarity of reviews and thus extracting a score out of it, which in collaboration with the product rating (which was available on a scale of 1 to 5) has helped build a more robust recommender system. We have deployed Quaestus on an e-commerce website that we have built. The paper describes the phases of implementation and shows the method to deploy our model to the website that we have created. The results after experiments have shown that our model fares better than other algorithms with which we have compared our model.

show abstract

On Web user tracking: How third-party http requests track users' browsing patterns for personalised advertising

Cited by 10 publications

References 15 publications

AdPExT: Designing a Tool to Assess Information Gleaned from Browsers by Online Advertising Platforms

AdPExT: Designing a Tool to Assess Information Gleaned from Browsers by Online Advertising Platforms

Web Tracking - A Literature Review on the State of Research

QUAESTUS – A Top-N Recommender System with Ranking Matrix Factorization

Contact Info

Product

Resources

About