Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such as categories. EB-NeRD served as the benchmark dataset for the RecSys '24 Challenge, where it was demonstrated how the dataset can be used to address both technical and normative challenges in designing effective and responsible recommender systems for news publishing. The dataset is available at: https://recsys.eb.dk.

The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group ("Ekstra Bladet"). The challenge explores the unique aspects of news recommendation, such as modeling user preferences based on behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Additionally, the challenge embraces normative complexities, investigating the effects of recommender systems on news flow and their alignment with editorial values. We summarize the challenge setup, dataset characteristics, and evaluation metrics. Finally, we announce the winners and highlight their contributions. The dataset is available at: https://recsys.eb.dk.

These groundbreaking models are designed to represent users and items as a bipartite, undirected graph, unlocking a whole new level of high-order relationships that were previously almost unattainable. Not only they do achieve better accuracy than their predecessors, but they are also setting a new standard for modern recommender systems [20, 28, 47, 79]. In recent years, great effort has been devoted in creating GNN-based models that address the critical issues of existing models, such as the over-smoothing phenomenon [12] and scalability issues [87]. These cutting-edge models are taking the world of recommender systems by storm and ushering in a new era of accuracy [41, 47, 51, 59, 81]. Over the past ten years, the application of neural techniques rooted in graph representation learning, such as graph convolutional networks [35] (GCNs), has introduced a fresh perspective on traditional collaborative filtering (CF) approaches. Rather than relying solely on user-item interactions for optimization [29, 36, 55], GCN-based methods enable the extraction of both short-and long-distance user preferences toward items [71]. By incorporating multi-hop relationships into the embeddings of users and items, these learned profiles yield more precise recommendations, as evidenced in the literature [28, 47].

Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs and preferences of individual users. In conversational recommendation approaches, these needs and preferences are acquired by the system in an interactive, multi-turn dialog. A common approach in the literature to drive such dialogs is to incrementally ask users about their preferences regarding desired and undesired item features or regarding individual items. A central research goal in this context is efficiency, evaluated with respect to the number of required interactions until a satisfying item is found. This is usually accomplished by making inferences about the best next question to ask to the user. Today, research on dialog efficiency is almost entirely empirical, aiming to demonstrate, for example, that one strategy for selecting questions is better than another one in a given application. With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation. This model, which is designed to cover a range of application scenarios, allows us to investigate the efficiency of conversational approaches in a formal way, in particular with respect to the computational complexity of devising optimal interaction strategies. Through such a theoretical analysis we show that finding an efficient conversational strategy is NP-hard, and in PSPACE in general, but for particular kinds of catalogs the upper bound lowers to POLYLOGSPACE. From a practical point of view, this result implies that catalog characteristics can strongly influence the efficiency of individual conversational strategies and should therefore be considered when designing new strategies. A preliminary empirical analysis on datasets derived from a real-world one aligns with our findings.