Thanks @edrex for putting us up on Product Hunt!
At Bibblio we're helping solve the problem of discovering the best content in a world of too much choice.
To do this we've launched a SaaS product that harnesses AI to enable publishers and content platforms to provide users with smarter, more relevant recommendations.
Our API uses natural language processing to quickly understand large volumes of content and identify key subjects and concepts. It then uses those subjects and concept to identify the most relevant recommendations for users as they interact with content on a platform. Unlike current recommender engines, like Taboola and Outbrain, it's optimised for relevance and diversity as well as popularity, so no filter bubbles and no clickbait!
We really appreciate the interest and feedback of the Product Hunt community, so we're giving away £500 of API credit to the first 200 sign-ups from Product Hunt - http://bibblio.org/producthunt - check it out!
@madsholmen@edrex Hey Mads, this sounds super interesting to me. I'm the founder of KYA (https://getkya.com) and we're a new kind of analytics platform for digital publishers/content creators. One of the additional features we offer is a super personalized content recommendation widget our customers can embed on their site to recirculate their content based on a reader's previous engagement on their site. I'd love to chat with you about Bibblio in more detail, perhaps there's a way we can work together or at least bounce ideas off each other. :)
Bibblio is structured serendipity, a tool to map knowledge. It allows you to truly discover rather than be served up what you already know. And their APIs are so damn clean.
Love the look of Bibblio - great job everyone involved.
It seems to me this fits into a wider trend of personalisation. How do you provide recommendations - and are they 'personalised'?
@edrex_ That's a really interesting question. Personalisation approaches are proving very popular, but currently they definitely have their limits - Demis Hassabis at DeepMind recently stated that “Personalisation doesn’t work very well. It currently sums up to averaging the crowd as opposed to adapting to the human individual”.
"Averaging the crowd" is what tends to push less relevant but more popular content to the top of recommended and related content lists. We actually wrote a blog post on this recently: "Popularity vs. Diversity" - https://medium.com/@bibblio_org/....
What we do aims to "adapt to the human individual" by finding what's most relevant to people as they are browsing content. By 'understanding' subjects and concepts in the content Bibblio can learn more about the key themes that interest users and look for other content which contains related themes (but in a much more sophisticated way than simple keyword matching). This means people are more likely to get recommendations that will interest and engage them, whilst ensuring they are exposed to diverse content.
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