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.
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.
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 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.
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.
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.
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.
<link rel="stylesheet" type="text/css" href="https://cdn.bibblio.org/rcm/4.7/bib-related-content.min.css">
<script id="bib--rcm-src" src="https://cdn.bibblio.org/rcm/4.7/bib-related-content.min.js" data-auto-ingestion="true" data-recommendation-key="INSERT_YOUR_KEY_HERE"/>
<div class="bib--rcm-init"
data-recommendation-key="INSERT_YOUR_KEY_HERE"
data-style-classes="bib--row-3 bib--hover bib--recency-show">
</div>
Related Posts by BibblioWordPress developers can run recommendations across posts in a few clicks. Your posts are indexed automatically, leaving you to just choose a module layout. Too easy.
