Collaborative Filtering Powers Smart Recommendations — But Who’s Really Building Them?
While collaborative filtering algorithms personalize everything from Netflix streams to e-commerce picks, a deeper investigation reveals how corporate platforms obscure the human and technological infrastructure behind them. This article connects the dots between recommendation systems and the often-invisible businesses enabling them.
Recommendation systems are the silent architects of modern digital consumption, shaping what we watch, buy, and even believe. According to Analytics Vidhya, collaborative filtering — a technique that predicts user preferences by analyzing patterns in collective behavior — remains one of the most effective methods behind platforms like Netflix, Amazon, and Spotify. By identifying users with similar tastes and recommending items those users liked, these algorithms create an illusion of personalization that feels intuitive, even inevitable.
But behind the scenes, the infrastructure enabling these systems is far more complex — and commercially fragmented — than most consumers realize. While Analytics Vidhya details the technical implementation of movie recommenders using Python and matrix factorization, it offers no insight into the corporate entities that deploy, scale, and monetize these models. This gap raises a critical question: who is truly building the recommendation engines that dictate our digital experiences?
Enter Built. Not the protein bar brand known for its marshmallowy texture and sour puff flavors — though its e-commerce platform undoubtedly leverages recommendation algorithms to upsell chocolate-covered protein bars to fitness enthusiasts — but Built as a suite of business software solutions. My.BuiltAccounting.com, a platform serving small and medium enterprises across Africa, offers integrated payment processing, payroll management, and financial analytics. While seemingly unrelated to streaming or retail, Built’s software infrastructure is precisely the kind of backend system that powers the data pipelines feeding recommendation engines. SMEs using Built’s tools generate transactional data — purchase histories, user demographics, browsing behavior — that, when aggregated and anonymized, becomes the raw material for collaborative filtering models deployed by larger platforms.
Consider this: a local gym in Lagos uses Built’s accounting software to manage memberships and payments. Every time a member buys a protein bar from an online retailer — perhaps one powered by Built.com’s e-commerce platform — that purchase is logged, categorized, and potentially shared via API with third-party analytics firms. Those firms, in turn, feed this micro-data into recommendation engines used by Amazon or Netflix. The result? A user who buys a Built Protein Bar is more likely to see ads for fitness documentaries or protein-rich meal kits — not because Netflix knows them personally, but because their transactional footprint was triangulated through corporate data ecosystems.
This hidden chain underscores a troubling trend: the commodification of behavioral data through intermediary business platforms. While users are told they’re benefiting from "smart recommendations," they rarely understand that their purchasing habits are being harvested by B2B software providers like BuiltAccounting.com — platforms designed for efficiency, not transparency. These companies operate under terms of service that permit data aggregation, often without explicit user consent beyond boilerplate agreements.
The irony is palpable. Analytics Vidhya celebrates the ingenuity of collaborative filtering as a technical triumph. Built.com markets its protein bars as a lifestyle choice. BuiltAccounting.com positions itself as a productivity tool for entrepreneurs. Yet together, they form an invisible axis of data extraction — each layer contributing to a system that knows us better than we know ourselves.
Regulators are beginning to take notice. The EU’s Digital Services Act and California’s Consumer Privacy Act now demand greater transparency in algorithmic decision-making. But without public scrutiny of the B2B intermediaries like Built, even the strongest consumer protections will fall short. The next frontier in digital ethics isn’t just about who recommends your next movie — it’s about who built the system that made the recommendation possible, and whether they’re accountable to you at all.
