A recent" third wave" of Neural Network (NN) approaches now delivers state-of-the-art
performance in many machine learning tasks, spanning speech recognition, computer …

Nowadays, in information systems such as web search engines and databases, diversity is
becoming increasingly essential and getting more and more attention for improving users' …

Sequential recommendation is an important recommendation task that aims to predict the
next item in a sequence. Recently, adaptations of language models, particularly Transformer …

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art
performance in many machine learning tasks, spanning speech recognition, computer …

One of the central issues in learning to rank for information retrieval is to develop algorithms
that construct ranking models by directly optimizing evaluation measures such as …

Fake news is being generated in different languages, yet existing studies are dominated by
English news. The analysis of fake news content has focused on lexical and stylometric …

Rankings, especially those in search and recommendation systems, often determine how
people access information and how information is exposed to people. Therefore, how to …

Existing work on search result diversification typically falls into the “next document”
paradigm, that is, selecting the next document based on the ones already chosen. A …

Maximum inner product search (or k-MIPS) is a fundamental operation in recommender
systems that infer preferable items for users. To support large-scale recommender systems …

In this paper we address the issue of learning diverse ranking models for search result
diversification. Typical methods treat the problem of constructing a diverse ranking as a …