Real-Time Recommendation of Streamed Data

This tutorial addressed two trending topics in the field of recommender systems research, namely A/B testing and real-time 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

Users' reading habits in online news portals

The aim of this study is to survey reading habits of users of an online news portal. The assumption motivating this study is that insight into the reading habits of users can be helpful to design better news recommendation systems. We estimated the transition probabilities that users who read an article of one news category will move to read an article of another (not necessarily distinct) news category. For this, we analyzed the users' click behavior within plista data set. Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category

CLEF 2017 NewsREEL 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 an overview over the implemented approaches and discusses the evaluation results. In addition, the main results of Living Lab and the Replay task are explained

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

Serotonin transporter polymorphism and stress effects on gut microbiota at various time points in pregnancy

Prenatal stress (stress experienced by the mother while pregnant) has been shown to greatly affect the health of a child. Still, not all offspring who experience prenatal stress will suffer poor outcomes. Maternal genetics and the timing of adverse events have been shown to interact to affect the likelihood that a child will be affected by prenatal stress. However, the mechanisms behind this interaction are not fully understood. One potential mechanism is the maternal gut microbiome (the community of bacteria residing in the mother's gut). The current study used a mouse model of genetic stress susceptibility, combined with a daily restraint stress during pregnancy to examine alterations in the maternal gut microbiome due to an interaction between genes and stress. While pregnancy was found to alter the microbiome differently at various time-points in pregnancy, there was no significant interaction between genes and stress. However, as the sample size was quite small and there were trending results, we believe there is good evidence to continue exploration of the effects of stress and genetics on the maternal microbiome.Dr. David Q. Beversdorf, Thesis Supervisor.Includes bibliographical references (pages 13-15)

The Relationship between Collection Strength and Student Achievement

This chapter examines how selected accrediting bodies and academic librarians define collection strength and its relationship to student achievement. Standards adopted by accreditation bodies and library associations, such as the Association of Research Libraries, are reviewed to determine the most common ones which are used to assess library collections. Librarians’ efforts to define and demonstrate the adequacy of library resources are also examined in light of increased focus on institutional accountability, and requirements to provide planned and documented evidence of student success. Also reviewed are the challenges and faced by academic librarians in a shift as they shift from traditional collection-centered philosophies and practices to those which focus on client-centered collection development such as circulation analysis, citation analysis, interlibrary loans and student satisfaction surveys to determine collection use and relevance. The findings from a review of standards and existing library literature indicated that student use of library collections depends on faculty perceptions of the library and whether they require students to use library resources and services for their research papers. Through marketing strategies, improvement of student awareness of collections and library services, the chapter concludes that multiple collection-related factors influence the academic success of students, not just the size and importance of library collections per se. The significance of the chapter lies in its identification of halting and difficult adjustments in measuring both collection “adequacy” and student achievements

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