Discovery and Promotion of Subtopic Level High Quality Domains for Programming Queries in Web Search

With the advancement of technology in modern era, a signi�cant portion of the web referred to as developer segment serves to satisfy the programming related information need of the users. User satisfaction in this segment not only depends on the relevance of the retrieved pages but also on the domains that these pages belong to. We aim to discover sub-topic level associations of the domains and queries. We propose a supervised deep neural network based approach using the click-through data of a commercial web search engine to discover and promote the domains which provide high quality and expert level content for a query intent. Experiments show that our domain speci�c ranker performs signi�cantly well, both qualitatively as well as quantitatively, on real-world coding query sets when compared with standard web ranking baseline. This paper further demonstrates how associating domains with query intents results in the formation of overlapping domain clusters where domains in each cluster represent a topical space of query intent(s).