Built for the future of content discovery
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.
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.
Our API endpoints give you unrestricted access to the Bibblio backend, so you can incorporate our algorithm stack into your existing framework.
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
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
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
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.
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
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
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
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.
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.
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.
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.
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.
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.
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