Building a food recommendation engine with Spark / MLlib and Play

Chimpler

Recommendation engines have become very popular in the last decade with the explosion of e-commerce, on demand music and movie services, dating sites, local reviews, news aggregation and advertising (behavioral targeting, intent targeting, …). Depending on your past actions (e.g., purchases, reviews left, pages visited, …) or your interests (e.g., Facebook likes, Twitter follows), the recommendation engine will present other products that might interest you using other users actions and user behaviors (page clicks, page views, time spent on page, clicks on images/reviews, …).

In this post, we’re going to implement a recommender for food using Apache Spark and MLlib. So for instance, if one is interested by some coffee products then we might recommend her some other coffee brands, coffee filters or some related products that some other users like too.

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