Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs

This paper summarises objectives, organisation, and results of the first news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted the evaluation of news recommendation algorithms in the form of a campaignstyle evaluation lab. Participants had the chance to apply two types of evaluation schemes. On the one hand, participants could apply their algorithms onto a data set. We refer to this setting as off-line evaluation. On the other hand, participants could deploy their algorithms on a server to interactively receive recommendation requests. We refer to this setting as on-line evaluation. This setting ought to reveal the actual performance of recommendation methods. The competition strived to illustrate differences between evaluation with historical data and actual users. The on-line evaluation does reflect all requirements which active recommender systems face in practise. These requirements include real-time responses and large-scale data volumes. We present the competition’s results and discuss commonalities regarding participants’ approaches

Benchmarking News Recommendations in a Living Lab

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

The plista dataset

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

Benchmarking news recommendations: the CLEF NewsREEL use case

The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms. The goal is to create an algorithm that is able to generate news items that users would click, respecting a strict time constraint. The lab challenges participants to compete in either a "living lab" (Task 1) or perform an evaluation that replays recorded streams (Task 2). In this report, we discuss the objectives and challenges of the NewsREEL lab, summarize last year's campaign and outline the main research challenges that can be addressed by participating in NewsREEL 2016

CLEF NewsREEL 2016: Comparing Multi-Dimensional Offline and Online Evaluation of News Recommender Systems

Running in its third year at CLEF, NewsREEL challenged participants to develop news recommendation algorithms and have them benchmarked in an online (Task 1) and offline setting (Task 2), respectively. This paper provides an overview of the NewsREEL scenario, outlines this year’s campaign, presents results of both tasks, and discusses the approaches of participating teams. Moreover, it overviews ideas on living lab evaluation that have been presented as part of a “New Ideas” track at the conference in Portugal. Presented results illustrate potentials for multi-dimensional evaluation of recommendation algorithms in a living lab and simulation based evaluation setting

CLEF NewsREEL 2017 Overview: Offline and Online Evaluation of Stream-based News Recommender Systems

The CLEF NewsREEL challenge allows researchers to evaluate news recommendation algorithms both online (NewsREEL Live) and offline (News-REEL Replay). Compared with the previous year NewsREEL challenged participants with a higher volume of messages and new news portals. In the 2017 edition of the CLEF NewsREEL challenge a wide variety of new approaches have been implemented ranging from the use of existing machine learning frameworks, to ensemble methods to the use of deep neural networks. This paper gives anoverview over the implemented approaches and discusses the evaluation results. In addition, the main results of Living Lab and the Replay task are explaineMultimedia Computin

CLEF NewsREEL 2017 Overview: Offline and Online Evaluation of Stream-based News Recommender Systems

The CLEF NewsREEL challenge allows researchers to evaluate news recommendation algorithms both online (NewsREEL Live) and offline (News-REEL Replay). Compared with the previous year NewsREEL challenged participants with a higher volume of messages and new news portals. In the 2017 edition of the CLEF NewsREEL challenge a wide variety of new approaches have been implemented ranging from the use of existing machine learning frameworks, to ensemble methods to the use of deep neural networks. This paper gives anoverview over the implemented approaches and discusses the evaluation results. In addition, the main results of Living Lab and the Replay task are explain

Workshop and challenge on news recommender systems

Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating itemcollections, 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

Evaluation-as-a-service for the computational sciences ::overview and outlook

Evaluation in empirical computer science is essential to show progress and assess technologies developed. Several research domains such as information retrieval have long relied on systematic evaluation to measure progress: here, the Cranfield paradigm of creating shared test collections, defining search tasks, and collecting ground truth for these tasks has persisted up until now. In recent years, however, several new challenges have emerged that do not fit this paradigm very well: extremely large data sets, confidential data sets as found in the medical domain, and rapidly changing data sets as often encountered in industry. Also, crowdsourcing has changed the way that industry approaches problem-solving with companies now organizing challenges and handing out monetary awards to incentivize people to work on their challenges, particularly in the field of machine learning. This white paper is based on discussions at a workshop on Evaluation-as-a-Service (EaaS). EaaS is the paradigm of not providing data sets to participants and have them work on the data locally, but keeping the data central and allowing access via Application Programming Interfaces (API), Virtual Machines (VM) or other possibilities to ship executables. The objective of this white paper are to summarize and compare the current approaches and consolidate the experiences of these approaches to outline the next steps of EaaS, particularly towards sustainable research infrastructures. This white paper summarizes several existing approaches to EaaS and analyzes their usage scenarios and also the advantages and disadvantages. The many factors influencing EaaS are overviewed, and the environment in terms of motivations for the various stakeholders, from funding agencies to challenge organizers, researchers and participants, to industry interested in supplying real-world problems for which they require solutions