Beyond Matrix Factorization: Using hybrid features for user-business recommendations
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Srivathsan Rajagopalan, Machine Learning Engineer
- Apr 25, 2022
Yelp’s mission is to connect people with great local businesses. On the Recommendations & Discovery team, we sift through billions of users-business interactions to learn user preferences. Our solutions power several products across Yelp such as personalized push notifications, email engagement campaigns, the home feed, Collections and more. Here we discuss the generalized user to business recommendation model which is crucial to a lot of these applications. High level overview of our recommendation system. Our previous approach for user to business recommendation was based on Spark’s Alternating Least Squares (ALS) algorithm which factorized the user-business interaction matrix to user-vectors and...