The Tech

Built for the future of content discovery

How it works

Bibblio technical workflow

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

Implementation - API or Quick Start

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

Recommendation type:
Optimized

Touchpoints:
On-site custom widget

See an example page >

News and media platform Mashable uses Bibblio's API to recommend their content across related articles using their own widget design.

Business model:
Advertising

Integration method:
API endpoints

Recommendation type:
Optimized

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

Recommendation type:
Related

Touchpoints:
On-site custom widget

See an example page >

Quick Start

If your budget is tight or you've limited tech resources, you can reduce bottleneck and accelerate time-to-value with Bibblio’s Quick Start code snippets. 

  • Use our JavaScript code 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.
  • 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 snippet

Clients using Quick Start

Combined news and social media platform The Article added Bibblio's Quick Start code to import and display their content recommendations using our pre-designed grid module.

Business model:
Advertising

Integration method:
Quick Start

Recommendation type:
Optimized

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

Recommendation types:
Optimized, Popular

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 via a Quick Start display snippet.

Business model:
Paywall

Integration method:
API endpoints/Quick Start

Recommendation type:
Optimized

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

Bibblio's algorithms use three data types...

Bibblio's Content, User and Behavior data types

...to generate four recommendation types...

Bibblio's Related recommendation type

Related

This displays semantically relevant recommendations for each content item. We calculate how similar or different content is from each other, from a contextual perspective, scoring each. Our setup allows us to generate quality recommendations at scale.

Ideal for "vertical discovery" where a site visitor is focused on the content and is keen to see more on that specific theme.

Bibblio's Popular recommendation type

Popular

Once some aggregate user behavior has been received from your site, this recommendation type suggests the content that's most likely to be clicked from across your corpus. It selects content items that are likely to generate a lot of clicks but aren't necessarily contextually similar to the content you're reading.

Ideal for "horizontal discovery" when a site visitor is browsing with a light touch.

Bibblio's Optimized recommendation type

Optimized

Our default recommendation type blends both of the algorithms that are used for Related and Popular, learning from your users’ interactions with the recommendations to predict which contextually relevant content item is most likely to be clicked for a specific source item. Essentially, the most clickable from the most related.

Ideal for ensuring a site visitor is being offered trending yet fitting suggestions.

Bibblio's Personalized recommendation type

Personalized

This type makes connections between content items and anonymized user data (such as user ID, click history, time, device, recency, etc.) to generate truly meaningful recommendations. Developed with content publishing in mind, the algorithm addresses key issues such as user cold-start, fast-growing quantities of content, accelerated decay of relevance, shifts in user preferences, and so on.

Ideal for engaging your loyal, high value users with suggestions that enhance their experience with you.

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.

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