Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …

Building a successful recommender system depends on understanding both the dimensions
of people's preferences as well as their dynamics. In certain domains, such as fashion …

Traditional studies on recommender systems usually leverage only one type of user
behaviors (the optimization target, such as purchase), despite the fact that users also …

Modern recommender systems model people and items by discovering orteasing apart'the
underlying dimensions that encode the properties of items and users' preferences toward …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

Recommending products to users means estimating their preferences for certain items over
others. This can be cast either as a problem of estimating the rating that each user will give …

Most existing recommender systems leverage user behavior data of one type, such as the
purchase behavior data in E-commerce. We argue that other types of user behavior data …

Recent studies on recommendation have largely focused on exploring state-of-the-art neural
networks to improve the expressiveness of models, while typically apply the Negative …