Abstract Learning-to-rank (L2R) based on feature selection has been proved effectively.
However, feature selection problem is more challenging due to two conflicting objectives …

Abstract Recommender Systems (RSs) make personalized suggestions of relevant items to
users. However, the concept of relevance may involve different quality aspects (objectives) …

We address the problem of automatically learning to classify texts by exploiting information
derived from meta-features, ie, features derived from the original bag-of-words …

Learning to Rank (L2R) is one of the main research lines in Information Retrieval. Risk-
sensitive L2R is a sub-area of L2R that tries to learn models that are good on average while …

Learning to Rank (L2R) is the core task of many Information Retrieval systems. Recently, a
great effort has been put on exploring Deep Neural Networks (DNNs) for L2R, with …

This paper addresses the feature selection problem in learning to rank (LTR). We propose a
graph-based feature selection method, named FS-SCPR, which comprises four steps:(i) use …

In information retrieval systems, search parameters are optimized to ensure high
effectiveness based on a set of past searches, and these optimized parameters are then …

A search engine generally applies a single search strategy to any user query. The search
combines many component processes (eg, indexing, query expansion, search-weighting …

Learning to rank has attracted increasing interest in the past decade, due to its wide
applications in the areas like document retrieval and collaborative filtering. Feature selection …

This article describes how feature selection for learning to rank algorithms has become an
interesting issue. While noisy and irrelevant features influence performance, and result in an …