We Have a Recommendation

A recommendation engine is a system that uses data analysis to suggest products, services, and information to users. In today’s world, these systems take data about user experiences, behaviors, preferences, and interests and put it to use saving consumers from the perils of information overload. The most obvious benefit of a recommendation engine is that it adds value to companies by improving user experience and visibly increasing revenues, click rates, and customer satisfaction. Netflix is one of the platforms where this effect can be seen most clearly. Thanks to its recommendation engine, Netflix users don’t have to browse through thousands of titles every time they want to watch something. Instead, they are presented first with the content that Netflix thinks will interest them the most, based on their viewing history—thus saving users’ time, ensuring a satisfying user experience, and saving Netflix about a billion dollars a year through customer retention. Although recommendation systems have been used by companies such as Amazon for nearly 20 years, it has only been in the past few years that companies in other sectors, such as finance and travel, have begun to wake up and take note.

  • Internet of the Mind: MindWeb

    At Tekhnelogos, we are designing our own multidisciplinary recommendation engine, MindWeb, for use by e-commerce sites. Each platform has its own particular customer behavior, which means that a one-size-fits-all recommendation engine will often fall short of fulfilling a company’s expectations. At the same time, there are some general tendencies that people exhibit across platforms, and recommendation engines must capture these if they are to be effective. In developing MindWeb, our team is analyzing both aspects of this equation to develop a recommendation engine that can operate across platforms while also offering a service that’s tailored to a specific company’s needs.

  • Big Data’s Big Impact

    Big data is vital to the design of projects like recommendation engines. Equally important, however, are the data mining and neural networks required to sift through and identify the patterns within all that data. And that’s what MindWeb does: collecting big data, cleaning and using that data, and identifying the patterns within it by feeding it into neural networks.