We present RML, the first known general reinforcement learning framework for relevance
feedback that directly optimizes any desired retrieval metric, including precision-oriented …

Existing pseudo-relevance feedback (PRF) methods often divide an original query into
individual terms for processing and select expansion terms based on the term frequency …

We propose AC-CRS, a novel conversational recommendation system based on
reinforcement learning that better models user interaction compared to prior work. Interactive …

Conversational search and recommendation systems can ask clarifying questions through
the conversation and collect valuable information from users. However, an important …

A major challenge in cross-language information retrieval (CLIR) is the adoption of
translation knowledge in retrieval models, as it affects term weighting which is known to …

Search engines are commonly present as information retrieval applications that help to
retrieve relevant information from different domain areas. The crucial part of improving the …

Word mismatch between queries and documents is a long-standing challenge in information
retrieval. Recent advances in distributed word representations address the word mismatch …

When information retrieval systems return a ranked list of results in response to a query, they
may be choosing from a large set of candidate results that are equally useful and relevant …

Pseudo relevance feedback (PRF) automatically performs query expansion based on top-
retrieved documents to better represent the user's information need so as to improve the …

Axiomatic analysis is a well-defined theoretical framework for analytical evaluation of
information retrieval models. The current studies in axiomatic analysis implicitly assume that …