Information Architecture: From the Early Web to Recommendation Engines

This Lab takes an exploratory look at the evolution of information architecture—how we design, categorize, and surface information online. From the static directories of the early internet, to the algorithm‑driven feeds of social media, and now to highly personalized recommendation systems, the underlying architecture of information has profoundly shaped how we learn, discover, and connect.

Origins: The Early Internet and Directories

In the early 1990s, the internet was small enough to be manually curated. Information architecture largely meant:

• Directories and lists: Yahoo! Directory (1994) was essentially a human‑edited taxonomy.
• Hierarchies: Websites often resembled file systems—clear menus, tree‑like navigation.
• Keywords: Search engines like AltaVista indexed pages but struggled with relevance.

The architecture reflected scarcity: there was less content, so organizing it in clear taxonomies was manageable. Discovery relied on browsing, not algorithmic personalization.

Search Engines: Scaling the Architecture

As the web exploded, taxonomies became insufficient. Enter Google (1998) and PageRank:

• Link analysis as architecture: Google treated links as signals of authority.
• Index and retrieval: Crawlers built massive indexes, structured around keyword relevance and link structure.
• Shift from hierarchies to networks: Architecture evolved from tree‑like menus to graph‑like connectivity.

Here, information architecture wasn’t just site navigation. It became about how the entire web was structured, with algorithms surfacing relevance.

Web 2.0 and Social Media: From Sites to Streams

With the rise of social networks (2004–2010), information architecture shifted again:

• Feeds over pages: Instead of navigating menus, users scrolled continuous streams.
• Folksonomy: Hashtags and user‑generated tagging replaced rigid taxonomies.
• Engagement as architecture: What rose to the top wasn’t necessarily authoritative but what generated reactions.

This was the era of architecture by activity. The structure of information bent around what users did—likes, shares, retweets—rather than fixed hierarchies.

Recommendation Engines: Personalization at Scale

By the mid‑2010s, platforms like Netflix, Amazon, YouTube, and TikTok perfected recommendation systems:

• Collaborative filtering: “People who liked X also liked Y.”
• Content‑based filtering: Matching items with similar attributes.
• Deep learning personalization: Neural nets predicting preferences at the individual level.

Here, the architecture of information became dynamic. There is no single, static hierarchy of content. Instead, each user’s view is a personalized architecture, constantly shifting based on behavior, context, and similarity to others.

This marks a radical departure: no two users inhabit the same architecture of information anymore.

The Importance of Information Architecture in Recommendations

Even as personalization grows, the underlying information architecture still matters:

• Metadata quality: Tags, categories, and structured data feed recommendation engines. Poor metadata leads to poor personalization.
• Ontologies and schemas: Knowledge graphs (like Google’s) provide semantic structure, enabling contextual recommendations.
• User experience: How content is surfaced, explained, and justified affects trust and adoption (“Why am I seeing this?”).
• Bias and diversity: Without careful architecture, recommendation systems create echo chambers or reinforce bias.

In short, even in an algorithmic era, the scaffolding of information—how it’s categorized, labeled, and interlinked—directly impacts personalization quality.

Today’s Architecture: Blended and Invisible

Modern platforms blur boundaries:

• Hybrid feeds: Mix of “For You” (algorithmic) and “Following” (chronological).
• Knowledge graphs and embeddings: Content is mapped into high‑dimensional spaces where similarity is architectural.
• Contextual personalization: Recommendations adapt not only to who you are, but when and where you are (time, device, location).

The architecture is now largely invisible. Users don’t see folders or menus; they see streams, suggestions, and search results. Architecture exists under the hood, as machine learning pipelines.

The Future of Information Architecture

Looking forward:

• Agentic navigation: AI assistants will increasingly mediate how users find content, creating conversation‑driven architectures.
• Dynamic ontologies: Information will reorganize in real time based on collective use, not static taxonomies.
• Transparency and control: Regulation and user demand may force platforms to reveal or let users tune their recommendation logic.
• Cross‑platform architectures: Instead of silos, federated systems (e.g., ActivityPub, decentralized graphs) may restructure discovery.

The future architecture of information will be adaptive, personalized, and contested—balancing discovery, diversity, and control.

Key Takeaways

• Early internet: Hierarchies and directories.
• Search era: Networks and link analysis.
• Social media: Feeds and activity‑based surfacing.
• Recommendation systems: Personalized, dynamic architectures.
• Now and next: Blended, invisible structures shaped by AI, data, and design choices.

Information architecture has always been more than site navigation—it’s the invisible substrate of discovery. In the age of recommendation engines, it has become both more powerful and more invisible, shaping not just how we find information, but what we believe the world looks like.

Reflection Questions

1. How has your own discovery of content changed across these eras?
2. What risks and opportunities come with personalized architectures?
3. How might businesses and creators design with both human IA principles and algorithmic personalization in mind?