ML Scoring: Where Machine Learning Meets...

Search can be viewed as a combination of a) A problem of constraint satisfaction, which is the process of finding a solution to a set of constraints (query) that impose conditions that the variables (fields) must satisfy with a resulting object (document) being a solution in the feasible region (result set), plus b) A scoring/ranking problem of assigning values to different alternatives, according to some convenient scale. This ultimately provides a mechanism to sort various alternatives in the result set in order of importance, value or preference. In particular scoring in search has evolved from being a document centric calculation (e.g. TF-IDF) proper from its information retrieval roots, to a function that is more context sensitive (e.g. include geo-distance ranking) or user centric (e.g. takes user parameters for personalization) as well as other factors that depend on the domain and task at hand. However, most system that incorporate machine learning techniques to perform classification or generate scores for these specialized tasks do so as a post retrieval re-ranking function, outside of search! In this talk I show ways of incorporating advanced scoring functions, based on supervised learning and bid scaling models, into popular search engines such as Elastic Search and SOLR. I'll provide practical examples of how to construct such "ML Scoring" plugins in search to generalize the application of a search engine as a model evaluator for supervised learning tasks. This will facilitate the building of systems that can do computational advertising, recommendations and specialized search systems, applicable to many domains.