As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

Traditional knowledge distillation approaches are typically designed for specific tasks, as
they primarily distilling deep features from intermediate layers of a neural network, generally …

Recent years have witnessed great success in handling graph-related tasks with Graph
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …

Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from
computing a ranking of final results based on a single metric to multi-objective problems …

In this work, a perturbation-based neural network (P-NN) scheme with an embedded
bidirectional long short-term memory (biLSTM) layer is investigated to compensate for the …

Graph Neural Networks (GNNs) can effectively capture both the topology and attribute
information of a graph, and have been extensively studied in many domains. Recently, there …

In online marketplaces, search ranking's objective is not only to purchase or conversion
(primary objective), but to also the purchase outcomes (secondary objectives), eg order …

Learning to rank models are broadly applied in ad hoc retrieval for scoring and sorting
documents based on their relevance to textual queries. The generalizability of the trained …

Quantization and pruning are two effective Deep Neural Networks model compression
methods. In this paper, we propose Automatic Prune Binarization (APB), a novel …

The Information Retrieval (IR) community has a rich history of empirically measuring novel
retrieval methods in terms of effectiveness and efficiency. However, as the search ecosystem …