Releasing datasets has fostered research in fields such as information retrieval and recommender systems. Datasets are typically tailored for specific scenarios. In this work, we present the plista dataset. The dataset contains a collection of news articles published on 13 news portals. Additionally, the dataset comprises user interactions with those articles. We inctroduce the dataset's main characteristics. Further, we illustrate possible applications of the dataset.

Abstract. Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys'13 and then as campaign-style evaluation lab NEWSREEL at CLEF'14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems.

Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating item collections, and highly sparse user profiles. This workshop (NRS'13@RecSys) brought together researchers and practitioners around the topics of designing and evaluating novel news recommender systems. Additionally, we offered a challenge allowing participants to evaluate their recommendation algorithms with actual user feedback.

Abstract. Providing high-quality news recommendations is a challenging task because the set of potentially relevant news items changes continuously, the relevance of news highly depends on the context, and there are tight time constraints for computing recommendations. The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms online and offline. In this paper, we discuss the objectives and challenges of the NewsREEL lab. We motivate the metrics used for benchmarking the recommender algorithms and explain the challenge dataset. In addition, we introduce the evaluation framework that we have developed. The framework makes possible the reproducible evaluation of recommender algorithms for stream data, taking into account recommender precision as well as the technical complexity of the recommender algorithms.

This tutorial addressed two trending topics in the field of recommender systems research, namely A/B testing and realtime recommendations of streamed data. Focusing on the news domain, participants learned how to benchmark the performance of stream-based recommendation algorithms in a live recommender system and in a simulated environment.