Predict relevance of search results from historical clicks using a Neural Click Model

Click models are algorithmic approaches which help in the ​understanding relevance of documents over a given query by modeling the search queries in a particular fashion. Currently, Wikimedia Search uses Dynamic Bayesian Network[DBN] which is based on the probabilistic graphical model. An algorithmic model, Neural Click Model [NCM] has been proposed, which is not only more accurate than DBN but also provides a way to input semantic features apart from click data. This project is about implementing, testing and analyzing NCM verify if it provides any computational or prediction benefits to the current model and finally integrating with the Mjolnir library.