The Tech

Built for the future of content discovery

Your complete toolkit, your way

Bibblio is an AI-driven content discovery platform that's built to enhance your audience's discovery experience.

The AI suggests your content using a blend of sophisticated recommender algorithms. Integrate, deliver and optimize this feed with a light-touch code snippet or go all in and customize with the full stack API.

Bibblio has anticipated the concerns surrounding online privacy, so does not require cookies. This eliminates any GDPR or browser blocking issues.

How you can implement Bibblio

Bibblio fits into the current tech layout
Content is processed through Bibblio's core algorithms

Cutting-edge AI at the core

Bibblio's AI engine recognizes any content written in a Latin-based alphabet. Our natural language processing understands each of your content items and quickly relates them all, thanks to our super-efficient streaming indexer.

The engine's machine learning constantly figures out and improves these connections, at scale, making it the most effective content recommender of its kind.

Use Bibblio's algorithms to show results based on the content's semantics, audience behavior or personalization data. Use these discretely or as a blend to produce recommendations for different business purposes.

Take a deeper dive into the algorithms

How it works

Bibblio technical workflow

Implementation - API or 'plug and play'

API endpoints

Our API endpoints give you unrestricted access to the Bibblio backend, so you can incorporate our algorithm stack into your existing framework.

  • Take control of the frontend experience to deliver recommendations at any touchpoint.
  • Organize your content into multiple catalogues to create specific discovery experiences.
  • Overlay business rules to stay on brand.
  • Integrate into your CMS for a faster deployment across all your media properties.
  • Adapt the AI learning to your users by incorporating unstructured metadata about your users and content.
  • Send us historical click data and additional endorsements about your content.
API endpoints

Clients using API endpoints

National Geographic uses Bibblio's API endpoints to recommend their catalogues of content, displayed within custom built panels on their encyclopedia site.

Business model:
Donation-funded non-profit

Integration method:
API endpoints

Bibblio algorithms:
Behavior

Touchpoints:
On-site custom widget

See an example page >

Experiential travel site AFAR.com uses Bibblio's API to recommend their catalogues of content across their articles, interlaced within their own promotional links widget.

Business model:
Advertising

Integration method:
API endpoints

Bibblio algorithms:
Behavior

Touchpoints:
On-site custom widget

See an example page >

The Business of Fashion serves recommendations to their related news articles via a custom designed widget that matches their site design, powered by Bibblio's API.

Business model:
Subscriptions/Advertising

Integration method:
API endpoints

Bibblio algorithms:
Behavior

Touchpoints:
On-site custom widget

See an example page >

Plug and play

If your budget is tight or you've limited tech resources, you can reduce bottleneck and accelerate time-to-value with Bibblio’s plug and play integrations. 

  • Use our Quick Start JavaScript snippet on your content pages to import your data to catalogues.
  • Display recommendations on your pages with our stylish and versatile related content module.
  • Choose from over 3,000 module design combinations or customize it with your own CSS.
  • If you run on WordPress, you can use our Related Posts plugin to import data and display our module.
  • User behavior is automatically tracked to better train the machine learning models. 
  • You can monitor module performance with Bibblio's top-level analytics.
  • Add tracking parameters that feed granular data to your own analytics platform.
Quick Start code snippetWordPress plugin

Clients using plug and play

SUITCASE Magazine uses Bibblio's WordPress plugin to import, index and display their content recommendations, altering the module design to their needs.

Business model:
Advertising

Integration method:
WordPress plugin

Bibblio algorithms:
Behavior

Touchpoints:
On-site Bibblio module

See an example page >

PhillyVoice - Philadelphia and South Jersey's online source for news - uses their total user activity to suggest their health content across all of their section pages and articles.

Business model:
Advertising

Integration method:
Quick Start code snippet

Bibblio algorithms:
Behavior

Touchpoints:
On-site Bibblio module

See an example page >

Despite using the API for importing content, Stratfor - the geopolitical intelligence platform - displays their recommendations on Bibblio's related content module.

Business model:
Paywall

Integration method:
API endpoints

Bibblio algorithms:
Behavior

Touchpoints:
On-site Bibblio module

See an example page >

Deeper dive

Content processing and enrichment

As soon as your content is imported to Bibblio it will automatically run through our semantic stack. This can be done asynchronously, before recommendations are added to your pages.

Bibblio uses standardized natural language processing to thoroughly analyze your content. This extracts relations, typed dependencies between words, and synonyms, that can be used in powerful context-aware semantic applications.

This metadata is mapped to the IPTC media taxonomy for our text categorization graph. This allows for richer, more complex mapping on all types of content items, regardless of their size.

 

Your content is processed with NLP

The algorithms unpacked

Bibblio's Context, Behavior and User algorithms

Context algorithm

This algorithm calculates contextual similarity - basically how similar or different your content is from each other, from a contextual perspective.

After enrichment, each metadata keyword receives a weighted relevance score for vectorization. This helps rank the recommendation sets for each source item. Our graph-based ANN version of TF-IDF allows us to generate quality recommendations at scale.

Bibblio's Context algorithm

Behavior algorithm

Once some user activity has been processed, our Behavior algorithm kicks in, learning from your users’ interactions with the recommendations to predict which content item is more likely to be clicked for a specific source item.

Instead of a static set of “popular” links that could be irrelevant to a user at that moment, our optimised algorithm offers suggestions based on user input, while still leaving a chance for old and new items alike.

The Behavior algorithm incorporates the Context algorithm, so recommendations will always be a blend of similar and popular content. This well-adjusted balance between contextual similarity and variety creates dynamic discovery experiences that keeps users engaged.

Bibblio's Behavior algorithm

User algorithm

Developed with content publishing in mind, Bibblio’s personalization algorithm is designed to tackle most of the issues encountered in this space: user cold-start, a fast growing number of content items, accelerated decay of content item relevance, shifts in user preferences, etc.

Applying a deep learning framework enables us to combine anonymised user and content IDs in order to generate highly meaningful connections using inputs such as history of clicked items, time, device, recency, and popularity.

Bibblio supports a fully customizable data schema for users, allowing you to pass us your most valuable user data to personalize the recommendations even further.

Bibblio's User algorithm

Getting up and running is smooth and fast

It's easy to get going with Bibblio. Copy and paste a code snippet onto your site, or opt for the freedom of full API access. There's even a WordPress plugin approved by WPEngine, getting you live in a couple of clicks.

Whichever method is best for you, our Support Center is open to quickly get you going. Our technical support team is also on hand to assist. 

Get started