- The Choice Is Yours. Or Is It?
In the pre-digital age and pre-Internet world, there were fewer choices and it was more difficult to make well-informed decisions. Relying only on one’s own judgment or a friend’s had mixed results and was often inaccurate. A wider net needed to be cast so more recommendations could be sorted through for the greatest and most accurate results.
Fast-forward to the digital age and the number of choices has become unlimited. Thanks to sites like Netflix, Pandora and Amazon, we can broaden our horizons without leaving the house.
A digital world brings bigger catalogs, which means there is an immediate choice-problem to be addressed, according to a Parks Associate’s Webcast featuring Rovi’s product development manager Michael Papish, Interpret LLC’s senior VP strategy and analysis Dan Casey and Digital Media Wire’s Ned Sherman.
Luckily, the solution, in the form of recommendation engines, exists and is being used by everyone for more things than we could possibly have imagined in the pre-Internet days.
To unlock the full potential of these recommendation engines, one must feed the beast. “Help me help you,” so to speak. Recommendation engines are maintenance heavy, require constant tweaking and perform best with human tuning. Perfection is an ongoing process. For best performance for a customer, users must build up their queue like Netflix, like or dislike songs on Pandora or purchase frequently from Amazon. The more content that is added, rated or bought, the more accurate recommendations can be tailored to fit individual tastes.
Along with users giving their personal preferences by physically making choices, users are also asked questions to find out their true desires. Engineers can’t simply ask what a user’s favorite movie is; that’s too general a question. In response, a user may panic and offer the most recent film they’ve seen or break from the pressure and answer with a popular classic. To find the truth, questions are more intimate and designed to search for the real connection a user has to content.
When making recommendations, creators have to be careful about what personal information they are based off of. If a recommendation is made based on gender, age or geographic location, a user may feel stereotyped and be turned off. Living in the South doesn’t necessarily mean a user likes country music. This is why more personal patterns are used, along with better questions.
Now that recommendations are widely used and welcomed, users feel they have more right to share their opinions and ratings on content. It has become a give-and-take relationship in which both parties are rewarded.
However, the price people are paying for such accurate and satisfying recommendations is the lack of being exposed to new, interesting and possibly unpleasant things. If we are spoon-fed only content we like and love, we’ll never try anything new, and a jaded society could be the result. This unknowing isolation has been named the Filter Bubble. Users stay in their bubble — their comfort zone — and are never exposed to anything challenging or exciting.
Are you prepared to let recommendation engines set the boundaries of your free will?
To quote Uncle Ben in Spiderman, “With great power, comes great responsibility.”
To balance this unfortunate development, the so-called “serendipity effect” is used. It recommends random content that the user will be forced to either accept or reject. This is a risky approach because users may be turned off by a selection, but at the same time, they may instead discover something they never knew they would enjoy.
The power to control our likes and dislikes and steer us toward one set of views or another is the great weight recommendation engines carry. If we’re not careful, recommendation engines may not only influence our choices (from right under our very noses) but start making them for us.
The choice is yours...or is it?
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