Recent years have witnessed the remarkable success of recommendation systems (RSs) in
alleviating the information overload problem. As a new paradigm of RSs, session-based …

The goal of intent-aware session recommendation (ISR) approaches is to capture user
intents within a session for accurate next-item prediction. However, the capability of these …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …

Session-based recommendation (SBR) aims to predict the following item a user will interact
with during an ongoing session. Most existing SBR models focus on designing sophisticated …

Session-based recommendation aims to predict intents of anonymous users based on their
limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence …

Session-based recommendation is devoted to characterizing preferences of anonymous
users based on short sessions. Existing methods mostly focus on mining limited item co …

Session-based Recommendation (SBR) has recently received significant attention due to its
ability to provide personalized recommendations based on the interaction sequences of …

Session-based recommendation aims to predict the next item based on a set of anonymous
sessions. Capturing user intent from a short interaction sequence imposes a variety of …

Session-based recommendation (SBR) faces the challenge of modeling user behavior
patterns within limited session sequences to predict the next item in anonymous sessions …

Session-based recommendation systems (SBRs) aim to capture user preferences over time
by taking into account the sequential order of interactions within sessions. One promising …