Mobile Recommendation Engines: Things are Heating Up

Mobile Recommendation Engines: Things are Heating Up image recommendation engineMobile Recommendation Engines: Things are Heating Up

The recommendation engine has become the hottest mobile craze since the tidal wave of Angry Birds knockoffs. This booming technology has encouraged developers to look for more accurate ways to predict what users want based on location, previous search history and even the device they’re using.

Location, location, location (and time of day)

Yelp, a recommendation engine pioneer, recently introduced an updated Nearby tab that lists Yelp-reviewed businesses according to the user’s location, previous check-ins and search history. But before they leave home, people can now get a broader definition of their city’s hottest neighborhoods with Yelp’s even newer word map. This quirky feature displays popular spots for things like bacon and hipsters, utilizing the keywords found in reviews.

Twitter is also moving into the space by acquiring localized search engine Spindle. Spindle, which will be absorbed into Twitter, not only recommends places to visit based on location, but also on the time of day. The thinking behind this is that you’re looking for different things on a Friday night than you are at lunch on Wednesday.

Different devices, different uses

People have been turning to the Internet for advice since the days of Ask Jeeves, but with the advent of smartphones, that old web-based butler is permanently unemployable. Mobile recommendation apps are so successful because they work around our schedule. Phones can be used at a moment’s notice to find relevant information quickly and let users get back to what they were doing. These on-the-go suggestions are perfect for impromptu dining and shopping decisions.

Tablets, on the other hand, are ideal for leisure activities like reading the news or watching TV. Whether someone’s killing time during a layover or laying in bed at night, apps like Yahoo’s IntoNow suggest what to cue up based on viewing patterns and the tastes of friends across social networks. Another recommendation engine, Prismatic, takes a different approach — it monitors personal social media usage to display relevant news stories. These examples show that no matter what users are looking to do, there’s an app for that.

The value of information

The main goal of every recommendation engine is to attract more users and, ultimately, more advertising dollars through sponsored results, but that won’t happen unless suggestions are actually helpful. Both Yelp and Twitter have done a great job amassing a huge amount of quality user-submitted content. But just like you wouldn’t go to a nudist for fashion advice, users won’t choose a recommendation app if every reviewer recommends against it. So before you build recommendations into your app experience, make sure you have the capability to do it right.

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