We all love beers. And today, we face an unprecedented variety of options. We are overwhelmed by too many choices and couldn’t decide: What should I drink next?
In our Hack Reactor thesis project, I build a machine learning server using PredictionIO as a recommendation engine for beer. Boardly speaking, the app is based on the following two strategies:
- Content filtering appoach — using each beer to charactize its nature. For example, we use breweryDB to find out the style of a specific beer, alcohol by volume (abv) and the International Bittering Units (ibu). Then we query the database to find beers with a similar style.
- Collaborative filtering approach — replies only on past user behaviour, i.e. your beer ratings. It is based on matrix factorization techniques with alternating least squares (ALS) algorithm. We characterize both beers and users by vectors of factors inferred from beer rating patterns. High correspondence between beer and user factors leads to a recommendation.
The advantage of using matrix factorization is that it allows incorperation of additional information. When explicit feedback is not availabe (i.e. your ratings), we can infer user preferences using implicit feedback, such as your browsing history and search pattern.
As a result, OnTapp matches you with beers that suit your taste. To get a recommendation of beer and try our our demo, plesae visit: http://ontappapp.com/
Experience in software development, application architecture, and deploying cloud solutions for enterprise customers. Strong hands-on skills with a Master's degree in Computer Science and business acumen with a master of business administration (MBA) in Finance. Certified in Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, Kubernetes (CKA, CKAD, CKS, KCNA) and Scrum (PSM, PSPO) with experience in building banking products from scratch.