Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

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

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: 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

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Applying topic model in context-aware TV programs recommendation

In IPTV systems, users’ watching behavior is influenced by contextual factors like time of day, day of week, Live/VOD condition etc., yet how to incorporate such factors into recommender depends on the choice of basic recommending model. In this paper, we apply a topic model in Information Retrieval (IR)–Latent Dirichlet Allocation (LDA) as the basic model in TV program recommender. What makes employing such approach meaningful is the resemblance between user watching frequency as the entry in user-program matrix and term frequency in term-document matrix. In addition, we propose an extension to this useroriented LDA by adding a probabilistic selection node in this probabilistic graphical model to learn contextual influence and user’s individual inclination on different contextual factors. The experiment using the proposed approach is conducted on the data from a web-based TV content delivery system “Vision”, which serves the campus users in Lancaster University. The experimental results show that both user-oriented LDA and context-aware LDA converge smoothly on perplexity regarding both iteration epoch and topic numbers under inference framework Gibbs Sampling. In addition, context-aware LDA can perform better than user-based LDA and baseline approach on both precision metrics and diversity metrics when the number of topic is over 50. Aside from that, programs with highest probability distribution within top 10 topics represent the natural clustering effect of applying this topic model in TV recommender

Algorithms Aside: Recommendation as the Lens of Life

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys

Mobile Phone Use and Risk of Uveal Melanoma: Results of the Risk Factors for Uveal Melanoma Case-Control Study

We recently reported an increased risk of uveal melanoma among mobile phone users. Here, we present the results of a case–control study that assessed the association between mobile phone use and risk of uveal melanoma. We recruited 459 uveal melanoma case patients at the University of Duisburg-Essen and matched 455 case patients with 827 population control subjects, 133 with 180 ophthalmologist control subjects, and 187 with 187 sibling control subjects. We used a questionnaire to assess mobile phone use and estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) of risk for uveal melanoma using conditional logistic regression. Risk of uveal melanoma was not associated with regular mobile phone use (OR = 0.7, 95% CI = 0.5 to 1.0 vs population control subjects; OR = 1.1, 95% CI = 0.6 to 2.3 vs ophthalmologist control subjects; and OR = 1.2, 95% CI = 0.5 to 2.6 vs sibling control subjects), and we observed no trend for cumulative measures of exposure. We did not corroborate our previous results that showed an increased risk of uveal melanoma among regular mobile phone users