At Atigeo we’ve been working on exciting new technology that enables businesses to go from guessing about consumers’ interests and needs to actually knowing about them. We’ve also figured out how to put users in control of their data to create a win/win situation for businesses and their customers.
Our technology combines explicit and implicit models of preference –stated preferences and consented observation of behavior respectively – to make recommendations based on its ability to learn about users’ preferences and interactions with those recommendations.
In doing so, we’ve developed some very interesting technology around inferring preferences – essentially a new mechanism for cold-start recommendations, the situation where no direct explicit or implicit evidence is available from which to deduce a user’s particular preferences, by exploiting associative, dynamically learned relevance and the comprehensive mining and understanding of a domain (e.g. sports, music, movies and entertainment, travel).
This last point helps us address the data sparseness issue that Rich MacManus’ mentions in the article referenced in my last post, namely how do you determine relevance to a profile with very few attributes? Our technology treats an implicit or explicit piece of data about an individual or an entity as defining an entry point in a vast interconnected hyperspace of knowledge about a given domain, the connectivity of which is determined both by a relevance structure learned in an unsupervised fashion from unstructured data, refined through a supervised learning via implicit behavioral feedback and interaction, and by evidence generated by explicit preferences and reasoned with using Bayesian statistics.
Our new technology has a broad range of possible applications. The question is, where will you see it first? Please stay tuned….