73,558 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
SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
In this work, we propose a new approach for discovering various relationships
among keywords over the scientific publications based on a Markov Chain model.
It is an important problem since keywords are the basic elements for
representing abstract objects such as documents, user profiles, topics and many
things else. Our model is very effective since it combines four important
factors in scientific publications: content, publicity, impact and randomness.
Particularly, a recommendation system (called SciRecSys) has been presented to
support users to efficiently find out relevant articles
Ontology-Based Recommendation of Editorial Products
Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
Millimeter-wave Evolution for 5G Cellular Networks
Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular
network requires evolution to increase the system rate 1000 times higher than
the current systems in 10 years. Motivated by this common problem, there are
several studies to integrate mm-wave access into current cellular networks as
multi-band heterogeneous networks to exploit the ultra-wideband aspect of the
mm-wave band. The authors of this paper have proposed comprehensive
architecture of cellular networks with mm-wave access, where mm-wave small cell
basestations and a conventional macro basestation are connected to
Centralized-RAN (C-RAN) to effectively operate the system by enabling power
efficient seamless handover as well as centralized resource control including
dynamic cell structuring to match the limited coverage of mm-wave access with
high traffic user locations via user-plane/control-plane splitting. In this
paper, to prove the effectiveness of the proposed 5G cellular networks with
mm-wave access, system level simulation is conducted by introducing an expected
future traffic model, a measurement based mm-wave propagation model, and a
centralized cell association algorithm by exploiting the C-RAN architecture.
The numerical results show the effectiveness of the proposed network to realize
1000 times higher system rate than the current network in 10 years which is not
achieved by the small cells using commonly considered 3.5 GHz band.
Furthermore, the paper also gives latest status of mm-wave devices and
regulations to show the feasibility of using mm-wave in the 5G systems.Comment: 17 pages, 12 figures, accepted to be published in IEICE Transactions
on Communications. (Mar. 2015
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