As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy. As such, this paper investigates whether more accurate recommendations from a ...

With the rise of algorithmic personalization, news organizations are finding it necessary to entrust traditionally held editorial values, such as prioritizing news for readers, to automated systems. In a case study conducted by Ekstra Bladet, the ...

Providing cold start recommendations in a brand-creator marketplace is challenging as brands’ preferences extend beyond the mere objects depicted in the creator’s content and encompass the creator’s individual theme consistently thatresonates across ...

The ultimate goal of recommender systems is satisfying users’ information needs in the long term. Despite the success of current recommendation techniques in targeting user interest, optimizing long-term user engagement and platform revenue is still ...

User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. The exploration of key interactive patterns between users and items has yielded significant improvements and great ...

On Spotify, multiple recommender systems enable personalized user experiences across a wide range of product features. These systems are owned by different teams and serve different goals, but all of these systems need to explore and learn about new ...

Graph Neural Networks (GNNs) have demonstrated significant potential in recommendation tasks by effectively capturing intricate connections among users, items, and their associated features. Given the escalating demand for interpretability, current ...

News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community. As these systems play an essential role in our daily lives, the mechanisms behind their curation processes are under ...

Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while ...

Collaborative filtering has been a dominant approach in the recommender systems community since the early 1990s. Collaborative filtering (and other) algorithms, however, have been predominantly evaluated by aggregating results across users or user ...

Recommender Systems are severely hampered by the well-known Cold Start problem, identified by the lack of information on new items and users. This has led to research efforts focused on data imputation and augmentation models as predominantly data pre-...

News media play an important role in democratic societies. Central to fulfilling this role is the premise that users should be exposed to diverse news. However, news recommender systems are gaining popularity on news websites, which has sparked concerns ...

With the rapid evolution of transformer architectures, researchers are exploring their application in sequential recommender systems (SRSs) and presenting promising performance on SRS tasks compared with former SRS models. However, most existing ...

Modern online platforms for user modeling and recommendation require complex data infrastructures to collect and process data. Some of this data has to be kept to later be used in batches to train personalization models. However, since user activity ...

Artificial Intelligence is transforming the news eco-system at a rapid pace. Large Language Models have emerged and facilitate producing content in larger quantities and with less skill or technical oversight. At the same time, media organizations ...

Evaluation is important when developing and deploying recommender systems. The PERSPECTIVES workshop sheds light on the different, potentially diverging or contradictory perspectives on the evaluation of recommender systems. Building on the discussions ...