27,055 research outputs found
On the Predictability of Talk Attendance at Academic Conferences
This paper focuses on the prediction of real-world talk attendances at
academic conferences with respect to different influence factors. We study the
predictability of talk attendances using real-world tracked face-to-face
contacts. Furthermore, we investigate and discuss the predictive power of user
interests extracted from the users' previous publications. We apply Hybrid
Rooted PageRank, a state-of-the-art unsupervised machine learning method that
combines information from different sources. Using this method, we analyze and
discuss the predictive power of contact and interest networks separately and in
combination. We find that contact and similarity networks achieve comparable
results, and that combinations of different networks can only to a limited
extend help to improve the prediction quality. For our experiments, we analyze
the predictability of talk attendance at the ACM Conference on Hypertext and
Hypermedia 2011 collected using the conference management system Conferator
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.
Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks
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