seldon-server

to recommendations. # Item Activity Correlation Built for static slowly changing historical inventory - Similar to Amazon’s “people who bought this also bought…” - Use historical user activity to find items that share similar user activity. - Apache Spark scalable offline implementation. - Upload for each item: top-N similar items. - For each user: item recommendations based on their historical activity. # Topic Models Built for sites needing long tail recommendation - Assume activity is associated with a set of topics. - Users individuals tastes are covered by a subset of topics. - Describe users by the set of keywords for the items they have interacted with. - Built with Apache Spark and Vowpal Wabbit implementation of Latent Dirichlet Allocation. - Online serving layer scores user association with items in real time. # Latent Factor Models Best for e-commerce sites lower churn sites - Netflix Prize-winning solution. - Use Matrix Factorization to reduce activity matrix to two low dimension user and item factor matrices. - Load factors into API servers and score users and items in real time. - Fold-in new users and items until next batch update of model. - Utilize Apache Spark mllib and streaming modules. # Content Similarity Built for services with rich metadata and high sparsity - Requirement – fast content based technique to match user history to similar content based on text/tags of content. - Utilize random vectors technique. Each word/tag is assigned a random high-dimensional vector. - Open-source Semantic-Vectors and word2vec implementations. - Periodically process recent content into vectors and update servers. - Servers load vectors into memory. - Recommendation on recent user activity to find similar content in real-time. (via http://seldon.io/algorithms)