Zurich startup launches first search engine based on taste preferences
Joixes are a taste researcher, an astrophysicist, a software developer and an e-commerce specialist. Together, they have developed a taste search engine and are revolutionizing Internet searches for products and services where aesthetics are the deciding factor. The innovative concept compiles search results according to the user's personal taste and sense of style, and aims to give the established search engine giants in the fields of fashion, restaurants, furniture and films a run for their money. The beta version is now available at joixes.com.
As we all know, there's no accounting for taste - our individual preferences are simply too different for that. Taste is also impossible to search for, because it's just difficult to put into words. At least until now. Because this is precisely where the established search engine giants are reaching their limits. This is where the visual search engine Joixes (www.joixes.com) comes in: Developed by four young people from Zurich, Joixes uses a short visual game to learn the user's tastes via simple swipe gestures and then suggests individual results that match his or her preferences. These are not based on traditional product specifications such as color, material or price, but on the user's aesthetic taste. Joixes thus finds relevant results even if the user can't quite describe exactly what he or she is looking for. And it does so simply by "swipe and see" instead of "type and read", i.e. with visual representations and simple selection options by swiping instead of manually typing in search terms.
Self-learning taste calibrator as a world first
"Taste can be understood as an individual preference for certain symbolic properties and associations that people attach great importance to when choosing products and services. Joixes is the first search engine that can easily and entertainingly capture and apply these preferences: Joixes is therefore also a kind of taste calibrator," says David Holzer, co-founder of Joixes and PhD in taste research. Those who want to use Joixes first determine their personal "taste profile" with a short visual quiz: the user selects image combinations that he or she likes - and that's it. A self-learning algorithm then selects the results that match the user's taste profile from millions of different options. Joixes also learns from the user's search behavior in order to constantly improve the results for each user: Yes-not-too-trendy bars for the big-city hipster on the hunt for the next insider tip, jean shorts, pompom sandals and hippie blouses for the summer girl with festival fever, or a long-forgotten French arthouse film for the culture aficionado.
Millions of products from over 400 vendors - from A for Amazon to Z for Zalando - provide choice
The results, which Joixes compiles individually for each user, come from around 400 product sources - including Zalando, Amazon, Foursquare and Yoox - and can be purchased directly with one click. "We are constantly expanding our network of sources from all sectors - in the furniture sector, for example, there are still many exciting platforms in our pipeline as sources," says Joixes co-founder Benjamin Boesch. "Our vision is to make Joixes the first port of call for anyone who would like to trust their taste when searching online, but isn't quite sure how to express it. In the future, we want to enable our users to create and manage a taste profile and apply it in different online and offline situations - for example, as a guideline for a hotel concierge when users ask him for tips on cool restaurants or sightseeing activities."
Designed for mobile use
Since Joixes is personalized exclusively via the anonymous taste profile, users do not have to disclose any sensitive data - a simple registration with an e-mail address is sufficient to always access the determined taste profile. Joixes is also completely ad-free - and will remain so - as it has been generating purely referral-based revenue since day 1. Joixes.com was developed mobile-first, i.e. specifically designed for searches on smartphones, to take into account the fact that the majority of all search queries are already made from mobile devices.