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Improving the Dining Experience Through Preference Algorithms

Alex Lorton

June 24, 2015


Tech giants like Amazon and Zappos have spent immense resources perfecting algorithms designed to track customer preferences and predict which items they would like to buy. After recently purchasing a soccer ball and a pair of soccer shorts, I found my Amazon home page full of suggestions for other soccer- and sports- related purchases. It was recommending items based on what I had bought and based on what others who had purchased similar items had also bought.

Preference algorithms could change the dining experience!

Understanding a customer’s preferences and giving him targeted suggestions is a staple of effective e-commerce. Unfortunately, the same cannot be said for dining. For an area of commerce as replete with options and as influenced by personal preference as food, systems for serving targeted suggestions are a huge potential opportunity.

E-commerce sites have two primary features that allow their preference algorithms to work: (1) the ability to track customer searches and purchases and (2) the ability to add metadata to the items that they sell. Metadata categorizes items by an array of characteristics, including size, color, brand, style, and much more. For instance, when I am logged into my Zappos account and search for brown boots, the site records this search. If I purchase a particular brand in a particular size, this is also recorded. Then, the next time I shop, I’m sure to see other products that have been determined to be similar or complementary to my past purchases.

How It Could Work

In the dining world, the ability to track searches and purchases, as well as the ability to add metadata to items is not common. However, it is something that if enacted, could mean big things for the way we eat.

Dining that takes place through websites or apps is ready and able to take advantage of preference algorithms. When ordering through a delivery service, a customer searches and purchases – and the service offers a variety of food items ready to be tagged with metadata. If I order a yellowtail jalapeño roll from a sushi restaurant, the system could infer that I have a predilection for spicy food and for fish. The next time I log in, I should see other suggestions for items or restaurants that match those tastes.

When Dining at Your Office

In my business,, we track corporate customers’ feedback on meals and individual dishes. Then, we use that information to determine what other sorts of meals they may like, thereby improving the dining experience. If a group indicates that they like meals that could be classified as healthy, we’ll serve them more healthy meals. If they show an interest in spicy dishes, we will up the heat for the next order.

Food Preference Algorithms

When Dining Out

The question that remains is how this applies to in-restaurant dining, where consumers don’t “log in” when they sit down at their table. How can we get targeted suggestions, based on past purchases and preferences, when at a table looking at a menu?

Technology can help us to achieve this end at a variety of different points.

  • Before even sitting down at the table, services like OpenTable can suggest restaurants we might like. They can do this by looking at the restaurants where we’ve booked in the past and potentially can suggest dishes if they integrate menu data and ask what we’ve liked from past dining experiences.
  • Once we’re at the table, restaurants can use menus on touch screen devices. They can ask eaters to log in or to simply answer a few questions about their preferences that would then show dishes that they may have a preference for.
  • After the meal, a simple question about whether or not they liked the dish can capture the information necessary to show future diners what others like them have enjoyed.

One Challenge

One potential stumbling block to making preference algorithms work well for food is the subjectivity of many of the dimensions upon which the dishes would be parsed. A dish that is spicy to some may be bland to others, just as a “healthy” dish to one person might be completely wrong for someone with a different definition.

However, just like standards were created for shoe or pants sizes, measures of objectivity can be added to food as well. The addition of nutrition information and ingredients in food can help to establish a variety of set objective measures. For example, the inclusion of a ghost pepper would qualify a dish as spicy, whereas a dish that’s 30% butter would not qualify as healthy.


Whether dining out or eating in, we can target a very tasty future of dining by focusing on preferences.


Originally published on Food Tech Connect, with Alex Lorton as a guest author.




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