Online advertising, as a vast market, has gained significant attention in various platforms
ranging from search engines, third-party websites, social media, and mobile apps. The …

This paper provides a review of the job recommender system (JRS) literature published in
the past decade (2011-2021). Compared to previous literature reviews, we put more …

Research on recommender systems algorithms, like other areas of applied machine
learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of …

Recommender systems are designed to help users in situations of information overload. In
recent years we observed increased interest in session-based recommendation scenarios …

The goal of a next basket recommendation (NBR) system is to recommend items for the next
basket for a user, based on the sequence of their prior baskets. We examine whether the …

Collaborative filtering models based on matrix factorization and learned similarities using
Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in …

Sequential recommendation problems have received increased research interest in recent
years. In such scenarios, the task is to suggest items to users to consume next, given their …

The main purpose of Sentiment Analysis (SA) is to derive useful insights from large amounts
of unstructured data compiled from various sources. This analysis helps to interpret and …

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for" In-
session prediction for purchase intent and recommendations". The challenge addresses the …

Click-through rate (CTR) prediction is a critical problem in web search, recommendation
systems and online advertisement displaying. Learning good feature interactions is …