Spending enormous sums on digital advertising on a monthly basis hurts, especially if your shop is not converting that precious traffic. As such, increasing your conversion rates by improving your on-site experience and product discovery should be the number 1 priority to increase your shop revenue in the long term. At the end of the day, investing in a smarter eCommerce search is probably the easiest starting point with the highest ROI. Visitors using your search bar on average have a 2x chance of actually buying an item. As competition is just one click away, you better provide those high-intent visitors with the best possible experience.

FACT-Finder, a company that offers ecommerce companies tools to personalize their site with things like AI-driven recommendations, said it has acquired Loop54, a company that provides personalized search results. It's the latest in a trend of consolidation in the ecommerce world, where a host of companies arose to offer personalization with new technologies like AI, but now the bigger companies are gobbling up the smaller ones -- and specifically in the ecommerce software-as-a-service (SaaS) search market. On the smaller side, we reported last week on Coveo's acquisition of AI-powered personalization provider Qubit. On the much bigger side, yesterday, reports emerged that PayPal is making a $45 billion bid for e-commerce giant Pinterest. "With the expertise and unique approach that our new colleagues at Loop54 bring to the table, we will significantly expand our market leadership and push the bounds of what is possible in e-commerce," said Emile Bloemen, CEO of FACT-Finder.

Information retrieval may suggest a document, and information extraction may tell us what it says, but which information sources do we trust and which assertions do we believe when different authors make conflicting claims? Trust algorithms known as fact-finders attempt to answer these questions, but consider only which source makes which claim, ignoring a wealth of background knowledge and contextual detail such as the uncertainty in the information extraction of claims from documents, attributes of the sources, the degree of similarity among claims, and the degree of certainty expressed by the sources. We introduce a new, generalized fact-finding framework able to incorporate this additional information into the fact-finding process. Experiments using several state-of-the-art fact-finding algorithms demonstrate that generalized fact-finders achieve significantly better performance than their original variants on both semi-synthetic and real-world problems.