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

Pseudo-relevance feedback based on the relevance model does not take into account the
inverse document frequency of candidate terms when selecting expansion terms. As a …

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 …

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

Pseudo-relevance feedback (PRF) refers to a query expansion strategy based on top-
retrieved documents, which has been shown to be highly effective in many retrieval models …

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 …

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

Recent developments of machine learning models, and in particular deep neural networks,
have yielded significant improvements on several computer vision, natural language …

A primary objective of every service provider system is to fulfill its users' needs [57, 140].
Irrespective of the nature of the service, encompassing domains such as media …

Pseudo-relevance feedback is a query expansion approach whose terms are selected from
a set of top ranked retrieved documents in response to the original query. However, the …