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Should Recommender Systems Be Exempt From the Post-Tracking Age? [Martin Anderson@Unite.ai
August 13, 2021
As first-party data gathering becomes the new lodestar for marketers and data brokers, the increased attention on ‘closed’ data-gathering systems risks to drag one of machine learning‘s most fervent research sectors down into controversy and greater regulation.
Actions taken by FAANG players and FOSS producers in the next 12-18 months are set to close down the culture of cross-domain tracking that engulfed user analytics systems over the past twenty years, and culminated in the Cambridge Analytica scandals and, subsequently, irresistible popular demand for increased online privacy.
Whether or not the implementation lives up to the ideal, and regardless of the extent to which more generalized tracking systems (such as Google’s FLOC and Apple’s SKAdNetwork) can assuage consumer ire and satisfy advertisers, this new wave of concern for user privacy applies only to cross-domain data extraction in a ‘public’ context, and not to closed or proprietary consumer environments, and the bespoke recommender systems that power engagement there.
Rich Data In Walled Gardens
Platforms such as Netflix, Disney+, HBO Max, Roku, and the Amazon ecostructure (including Prime Video and product recommendations), which utilize custom-built machine learning recommendation systems, are among the content services now proliferating and retrenching as the streaming industry balkanizes.
As third-party data-gathering recedes, the advantage these larger streaming players retain in terms of fine-grained access to customer usage data seems likely to inspire envy and imitation, and a renewed emphasis on first-party frameworks as a way of clawing back hyper-personalized targeting from the more generalized new analytics systems.
If this happens, it isn’t likely to be as democratic or meritocratic as prior criteria for entry, because the biggest advantage will fall to providers with the most extensive network of first-party platforms; with enough development resources to provide secure local authentication systems; and which are able to manage, analyze and monetize high volume data locally.
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries.
We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).