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 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. 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/4.9/bib-related-content.min.css">
<script id="bib--rcm-src" src="https://cdn.bibblio.org/rcm/4.9/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.
