Recommender Systems (RS) are a subclass of information filtering systems that seek to
predict the rating or preference a user would give to an item. e-Recruitment is one of the …

There exist situations of decision-making under information overload in the Internet, where
people have an overwhelming number of available options to choose from, eg products to …

Combinatorial features are essential for the success of many commercial models. Manually
crafting these features usually comes with high cost due to the variety, volume and velocity …

Various factorization-based methods have been proposed to leverage second-order, or
higher-order cross features for boosting the performance of predictive models. They …

Click-Through Rate prediction is an important task in recommender systems, which aims to
estimate the probability of a user to click on a given item. Recently, many deep models have …

In implicit feedback-based recommender systems, user exposure data, which record
whether or not a recommended item has been interacted by a user, provide an important …

Recommender systems suggest items that users might like according to their explicit and
implicit feedback information, such as ratings, reviews, and clicks. However, most …

Modern recommender systems are trained to predict users' potential future interactions from
users' historical behavior data. During the interaction process, despite the data coming from …

In various web applications like targeted advertising and recommender systems, the
available categorical features (eg, product type) are often of great importance but sparse. As …

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 …