53 research outputs found
Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders
In the vast and expanding ocean of digital content, users are hardly satisïŹed 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 eïŹective 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 âVisionâ. Within this recommender, selection criteria of candidate ïŹelds and contextual factors are designed and usersâ dependencies on their personal pref-erence or the aforementioned contextual inïŹuences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, ïŹnal experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be ïŹexibly used for diïŹerent 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 RecSysâ13 and then as campaign-style evaluation lab NEWSREEL at CLEFâ14. 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 competitionâs results and discuss commonalities
regarding participantsâ 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 yearâs 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 âNew Ideasâ 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 satisïŹed 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 eïŹective 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 âVisionâ. Within this recommender, selection criteria of candidate ïŹelds and contextual factors are designed and usersâ dependencies on their personal pref-erence or the aforementioned contextual inïŹuences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, ïŹnal experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be ïŹexibly used for diïŹerent recommendation purposes
Applying topic model in context-aware TV programs recommendation
In IPTV systems, usersâ 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)âLatent 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 userâs 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 âVisionâ, 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
Grade Level Filtering for Learning Object Search using Entity Linking
More and more Learning Objects like lessons, exercises, worksheets and lesson plans are available online. Finding them, however, is a challenge as they often lack metadata concerning format, content and, in the K-12 context: grade-levels or age ranges for which they are appropriate. This work studies the automatic content-based assignment of this last aspect of Learning Object metadata. For this purpose, we (a) collected a dataset of physics lessons, (b) explored a set of text-based features for their automatic analysis (derived from both dense vector representations and entity linking methods) and (c) trained a machine learning model with different subsets of these features to predict a resourceâs target grade level. We compare and discuss the results
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