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 recommendation (SR) specializes in users’ short-term preferences and aims to ...

Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment opportunities continually, which limits the performance of job ...

With mounting privacy concerns, and movement towards a cookie-less internet, session-based recommendation (SR) models are gaining increasing popularity. The goal of SR models is to recommend top-K items to a user by utilizing information from past ...

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 neural-based encoders to learn a session representation, capturing the ...

Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented by ...

Session-based recommendation predicts users' future interests from previous interactions in a session. Despite the memorizing of historical samples, the request of unlearning, i.e., to remove the effect of certain training samples, also occurs for ...

Session-based recommendation, a specific type of recommendation system, leverages users’ interaction sequences to provide recommendations. Unfortunately, these approaches tend to overlook user privacy protection and are susceptible to session ...

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item ...

Session-based recommendation (SBR) aims to predict the anonymous user's next-click items by modeling the short-term sequence pattern. As most existing SBR models generally generate item representations based only on information propagation over the short ...

On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving user interests, cloud-based recommender systems are periodically updated ...

Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music. However, the existing recommender systems overlook the unique ...

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 approach within this domain is session graph-based recommendation, which ...

Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users. The challenges facing SBR consist mainly of how to ...

Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of ...

Aiming at a better understanding of the search goals in the user search sessions, recent query recommender systems explicitly model the reformulations of queries, which hopes to estimate the intents behind these reformulations and thus benefit the next-...

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily ...

Session-based recommendation aims to predict the user's next action based on anonymous sessions without using side information. Most of the real-world session datasets are sparse and have long-tail item distribution. Although long-tail item ...

Session-based recommendation (SBR) has received increasing attention to predict the next item via extracting and integrating both global and local item-item relationships. However, there still exist some deficiencies in current works when capturing these ...

Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (...

Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence ...