Being able to check whether an online advertisement has been targeted is essential for resolving privacy controversies and implementing in practice data protection regulations like GDPR, CCPA, and COPPA. In this paper we describe the design, implementation, and deployment of an advertisement auditing system called iWnder that uses crowdsourcing to reveal in real time whether a display advertisement has been targeted or not. Crowdsourcing simplifies the detection of targeted advertising, but requires reporting to a central repository the impressions seen by different users, thereby jeopardising their privacy. We break this deadlock with a privacy preserving data sharing protocol that allows iWnder to compute global statistics required to detect targeting, while keeping the advertisements seen by individual users and their browsing history private. We conduct a simulation study to explore the effect of different parameters and a live validation to demonstrate the accuracy of our approach. Unlike previous solutions, iWnder can even detect indirect targeting, i.e., marketing campaigns that promote a product or service whose description bears no semantic overlap with its targeted audience.
Topic-based solutions perform content-based analysis to extract the relevant topics on a user's browsing history and the ads he receives. Then, using different heuristics and statistical means, targeted ads are identified as those having topics that share some semantic overlap with the user's browsing history. Topic-based detection could, in principle, be applied to real users, as we have done for evaluation purposes in . Existing work, however, has only used it in conjunction with artificially constructed , , robots that browse the web imitating very specific (single-topic) demographic groups @cite_14 @cite_17 , or to emulate real-users offline using click-streams @cite_5 .
@cite_5 @cite_14 @cite_17
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The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search. Computing the web-user's relevance to a give topic is an important task for any personalization service on the Web. Since the interest and preference of a web-user are revealed in his Web browsing history, in this paper we develop a novel approach that utilizes Web logs to compute the relevance of a web-user to a given query. In contrast to traditional methods that are purely based on textual analysis, our approach calculates the web-user's relevance through link analysis under a unified framework where the importance of web-pages and web-users mutually reinforce each other in an iterative way. The experimental results show that our approach has achieved 53 of accuracy when ranking the web-user's relevance to a search topic. User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users interest profiles via tracking their online behaviour and then delivers the relevant ads according to each users interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.