The internet is ubiquitous. It's mostly paid for by advertising. Our season explores the algorithms, technology, platforms, and perspectives on the world of real time digital advertising.
Increasingly, people get most if not all of the information they consume online. Alongside the web sites, videos, apps, and other destinations, we’re consistently served advertisements alongside the organic content we search for or discover. Targetted ads make it possible for you to discover relevant new products you might otherwise not have heard about. Targetting can also open a pandora’s box of ethical considerations. Online advertising is a complex network of automated systems. Algorithms controlling algorithms controlling what we see.
Effectively managing a large budget of pay per click advertising demands software solutions. When spending multi-million dollar budgets on hundreds of thousands of keywords, an effective algorithmic strategy is required to optimize marketing objectives.
Ravi Krishna joins us today to talk about his recent work on a differentiable NAS framework for ads CTR prediction. He discussed what CTR prediction is about and why his NAS framework helps in building neural networks for better ads recommendation. Listen to learn about methodology, related literature and his results.
Have you ever wondered what goes on under the hood when you accept a website’s cookies? Today, Maximilian Hils, a PhD student in Computer Science, at the University of Innsbruck, Austria, dissects the ad tech industry and the standards put in place to protect users’ data. He also shares his thoughts on the use of VPNs as well as other tools that help shield your data from prying eyes on the internet.
NaLette Brodnax, a political scientist and an Assistant Professor in the McCourt School of Public Policy at Georgetown University joins us to discuss her work on analyzing digital advertisements for political campaigns. She used data for electoral campaigns on Facebook to answer questions that help us better understand how digital ads affect the outcome of elections.
Eric Zeng joins us to discuss his study around understanding bad ads and efforts that can be taken to limit bad ads online. He discussed how he and his co authors scrapped a large amount of ad data, applied a machine learning algorithm, and commensurate statistical results.
Cameron Ballard joins us today to discuss his work around YouTube conspiracy theories. He revealed interesting observations about conspiracy theories on YouTube including how predatory ads are most common in conspiracy theory videos and how YouTube’s algorithm subtly works for predatory ads.
Affiliate marketing creates an opportunity for marketers to gain a commission by promoting a product or service. Cookies are typically used for tracking and the advertiser whose product or service is being featured pays the marketing only on transactions.
Today, we are joined by Piotr Niedźwiedź, Founder and CEO of Neptune.ai. Piotr discusses common MLOps activities by data science teams and how they can take advantage of Neptune.ai for better experiment tracking and efficiency. Listen for more!
Rajan Udwani, an Assistant Professor at the University of California Berkeley joins us to discuss his work on AdWords with unknown budgets. He discussed the previous approaches to ad allocation, as well as his maiden approach that introduced randomization for better results. Listen for more.
While we give attention to textual data on the web, many do not know the unique power of echo interactions with smart devices for ad targeting. Today, our guest, Umar Iqbal joins us to discuss his study on using Amazon Smart Speakers for ad targeting. He gave interesting revelations about how voice data is captured and analysed for ad purposes. Listen to find out more.
Chances are that you have bought a product online majorly because of the reviews you saw. Unfortunately, not all reviews are genuine. Today, Rajvardhan Oak shares some insight from his research on fraudulent Amazon reviews. He explained the inner workings of fraudulent reviews and revealed key insights from his qualitative and quantitative study.
When we search for products in e-commerce stores, we do not care what goes on under the hood to generate the results. However, there may be an intentional algorithmic effort to gravitate us toward a particular product. On the show, today, Abhisek Dash and Saptarshi Ghosh discuss their research on fairness in the search result of Amazon smart speakers.
Growing your podcast to the point of monetization is not a walk in the park. Today, Rob Walch, the VP of Podcast Relations at Libsyn talks about podcast advertising. He discussed how advertising works, how to grow your audience and some blueprints to being a successful podcaster. Listen for more.
Liza Gak, a Ph.D. student at UC Berkeley, joins us to discuss her research on harmful weight loss advertising. She discussed how weight loss ads are not fact-checked, and how they typically target the most vulnerable. She extensively discussed her interview process, data analysis, and results. Listen for more!
When you accept cookies on a website, you cannot tell whether the cookies are used for tracking your personal data or not. Shaoor Munir’s machine learning model does that. On the show today, the Ph.D student at the University of California, discussed the world of first-party cookies and how he developed a machine learning model that predicts whether a first-party cookie is used for tracking purposes.
Data sharing in the ad tech space has largely been a black box system. While it is obvious the data is being collected, the data sharing process is obscure to users. On the show today, Maaz Bin Musa and Rishab, both researchers at the University of Iowa, speak about the importance of data transparency and their tool, ATOM for data transparency. Listen to find out how ATOM uncovers data-sharing relationships in the ad-tech space.
Moses Guttman from Clear ML joins us to share insights about how organizations leveraging machine learning keep their programs on track. While many parallels exist between the software development life cycle (SWLC) and the machine learning development life cycle, successful deployments of ML in production have demonstrated that a unique set of tools is required. Moses and I discuss the emergence of ML Ops, success stories, and how modern teams leverage tools like Clear ML's open source solution to maximize the value of ML in the organization.
We hear about the impeccable achievements of GPT-3 models, but such large generative models come with their bias. On the show today, Conrad Borchers, a Ph.D. student in Human-Computer Interaction, joins us to discuss the bias in GPT-3 for job ads and how such large models can be de-biased. Listen to learn more!
Peter Gloor, a Research Scientist at the MIT Center for Collective Intelligence, takes us on a new world of tribe classification. He extensively discussed the need for such classification on the internet and how he built a machine learning model that does it. Listen to find out more!
The advancement of generative language models has been a force for good, but also for evil. On the show, Avisha Das, a post-doctoral scholar at the University of Texas Health Center, joins us to discuss how attackers use machine learning to create unsuspecting phishing emails. She also discussed how she used RNN for automated email generation, with the goal of defeating statistical detectors.
People who do not want their data tracked and shared online can pay a token for a cookie paywall. But are the websites keeping to their side of the bargain? Victor Morel, a Postdoc candidate at the Chalmers University of Technology joins us to discuss his work around auditing the activities of cookie paywalls. He discussed the findings from his analysis and proffers some solutions to making cookie paywalls more transparent.
While at first glance, the use of ad blockers drops the revenue of news publishers, this may not be completely true. On the show today, Shunyao Yan, an Assistant Professor in Marketing at Leavey School of Business, Santa Clara University, discussed the effect of ad blockers on news consumption and how ad blockers can potentially be helpful for news publishers.
On the show, Aleksandra Urman and Mykola Makhortykh join us to discuss their work on the comparative analysis of web search behavior using web tracking data. They shared interesting results from their analysis, bordering around the user preferences for search engines, demographic patterns, and differences between how men and women surf the net.
When we navigate a webpage, it is fairly easy for our mouse movement to be tracked and collected. Today, Luis Leiva, a Professor of Computer Science discusses how these mouse tracking data can be used to predict age, gender and user attention. He also discusses the privacy concerns with mouse tracking data and possible ways it can be curtailed.
We are joined by Anthony Katsur, the CEO of IAB Tech Lab. Anthony discusses standards within the ad tech industry. He explained how IAB Tech Lab set and propagates global standards, actions to ensure compliance from advertisers, and industry trends for a more privacy-centric ad tech space.
Kerstin Bongard-Blanchy is a Research Associate at the University of Luxembourg. She joins us to discuss her study that investigated dark patterns in web designs. She discussed the results, the effect of dark patterns effect on users, whether an average user can detect them, and the way forward to a more ethical web space.