There’s a lot of information on the Internet – good, bad, and some irrelevant. Sifting through all that clutter to get what you are looking for has become a burning necessity for Internet-based entities. The solution? Filtered content and personalised algorithms.
“Emerging research underscores the substantial value personalised advertisements yield, contributing billions of dollars to publisher earnings,” Aayush Mudgal, a senior ML expert at Pinterest told AIM.
The conversation hinted towards Google and Meta which are heavily reliant on its advertisement revenues for filling their pockets. In 2023 alone, Google made a whopping $162 billion from its ad business, 58% of its overall revenue. Even Meta’s ad revenues are estimated at $153.76 billion in 2023, a 13.1% increase from 2022.
At the image sharing platform, Mudgal is the tech lead for privacy aware conversion modelling and focuses on ads ranking. He holds expertise in large-scale recommendation systems, personalization, and ads marketplace.
He continued, “Personalization equips advertisers with the ability to display pertinent products and services, setting the stage for an engaging ad experience that augments customer satisfaction while safeguarding consumer privacy, which is a priority in our evolving digital age.”
How Pinterest Strikes the Sweet Balance
Same as other social media platforms, Pinterest brings in cash mainly by inviting businesses to advertise on its platform. But here, the platform lets the users choose whether they want to see personalised ads or not through their ‘Do not track’ feature.
Founded in 2010, Pinterest struggled at first to gain traction and followed by a struggle to deal with bias. But over the years, the engineers redesigned its systems and retrained its algorithms to better target diverse users and map their interests. As the platform evolved over the years, striking a sweet spot between making money and not exploring user data, its algorithms are often lauded for avoiding scandals around recommendation bias its rivals continue to face today.
In regards to the ongoing paradigm shift in the digital landscape, Mudgal advises that “moving away from individual third-party data has become ever more apparent. Notable developments within the industry show accelerated interest in privacy-enhancing methodologies, such as differential privacy, federated learning, and homomorphic encryption, as well as de-identified learning.” He noted that these methods safeguard the confidentiality of sensitive data majorly dealt with.
Even though these approaches found their way in finance and medicine, recently they have become important in advertising due to an increased emphasis on privacy.
Elaborating the need for these methods, Mudgal stated that these solutions strike a balance between tailoring personalization and preserving user privacy. “As the momentum for privacy within the advertising domain continues to escalate in strength, the utilisation of these methodologies will likely emerge as the standard in the ad industry,” he added.
The delicate act of balancing personalization and preventing filter bubbles in recommendation systems presents an intricate challenge. Nonetheless, it’s a crucial undertaking, ensuring users are exposed to a diverse, pertinent content spectrum, thus averting potential isolation within like-minded echo chambers, Mudgal pointed out.
Privacy Talks
In the series of privacy debates, the latest development has been made by Google with its project Privacy Sandbox. Through the project, Google aims to mine users’ browsing histories to support its own advertising profits by kicking out third party advertisers. In short, it means websites can fetch your online interests straight from your browser.
“As advertising evolves, privacy will always be top of mind for the entire tech industry,” believes Mudgal. The IIT Kanpur graduate noted that with the evolving privacy regulatory landscape, digital advertising must become less reliant on individual third party data, and be more privacy safe.
He further explains how advertisers can reach people on the platform who are more likely to take the business desired action. “This leverages ML models that help serve ads to the people we believe are the most likely to convert. Models emphasise on platform signals and extrapolation techniques, reducing reliance on offsite data,” he added.
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