Debasis Ganguly

Modern information retrieval systems typically operationalise relevance as a query-conditional score computed at inference time. This design choice has become dominant such that alternative decompositions of relevance are rarely discussed, despite the long history of document and query priors in...

Conversational search systems help users satisfy complex information needs through natural language interactions, yet incorporating user preferences into ranking remains challenging. Existing rewrite-then-rerank pipelines capture topical relevance but struggle with fine-grained constraints such a...

Second-stage neural rankers are commonly applied with a fixed re-ranking depth, assuming that retrieval effectiveness saturates as depth increases. Prior research has questioned the assumption that increasing the re-ranking depth yields linear performance gains, further suggesting that optimal re...