Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

Modeling users' dynamic preferences from their historical behaviors is challenging and
crucial for recommendation systems. Previous methods employ sequential neural networks …

The problem of session-based recommendation aims to predict user actions based on
anonymous sessions. Previous methods model a session as a sequence and estimate user …

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user
clicking on the item, has become one of the core tasks in the advertising system. For CTR …

In order to model the evolution of user preference, we should learn user/item embeddings
based on time-ordered item purchasing sequences, which is defined as Sequential …

Predicting users' actions based on anonymous sessions is a challenging problem in web-
based behavioral modeling research, mainly due to the uncertainty of user behavior and the …

Sequential Recommendationdescribes a set of techniques to model dynamic user behavior
in order to predict future interactions in sequential user data. At their core, such approaches …

With the revival of neural networks, many studies try to adapt powerful sequential neural
models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based …