17,346 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
VIQID: a no-reference bit stream-based visual quality impairment detector
In order to ensure adequate quality towards the end users at all time, video service providers are getting more interested in monitoring their video streams. Objective video quality metrics provide a means of measuring (audio)visual quality in an automated manner. Unfortunately, most of the current existing metrics cannot be used for real-time monitoring due to their dependencies on the original video sequence. In this paper we present a new objective video quality metric which classifies packet loss as visible or invisible based on information extracted solely from the captured encoded H.264/AVC video bit stream. Our results show that the visibility of packet loss can be predicted with a high accuracy, without the need for deep packet inspection. This enables service providers to monitor quality in real-time
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Crowdsourcing platforms are now extensively used for conducting subjective
pairwise comparison studies. In this setting, a pairwise comparison dataset is
typically gathered via random sampling, either \emph{with} or \emph{without}
replacement. In this paper, we use tools from random graph theory to analyze
these two random sampling methods for the HodgeRank estimator. Using the
Fiedler value of the graph as a measurement for estimator stability
(informativeness), we provide a new estimate of the Fiedler value for these two
random graph models. In the asymptotic limit as the number of vertices tends to
infinity, we prove the validity of the estimate. Based on our findings, for a
small number of items to be compared, we recommend a two-stage sampling
strategy where a greedy sampling method is used initially and random sampling
\emph{without} replacement is used in the second stage. When a large number of
items is to be compared, we recommend random sampling with replacement as this
is computationally inexpensive and trivially parallelizable. Experiments on
synthetic and real-world datasets support our analysis
Information extraction from multimedia web documents: an open-source platform and testbed
The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval
Advanced solutions for quality-oriented multimedia broadcasting
Multimedia content is increasingly being delivered via different types of networks to viewers in a variety of locations and contexts using a variety of devices. The ubiquitous nature of multimedia services comes at a cost, however. The successful delivery of multimedia services will require overcoming numerous technological challenges many of which have a direct effect on the quality of the multimedia experience. For example, due to dynamically changing requirements and networking conditions, the delivery of multimedia content has traditionally adopted a best effort approach. However, this approach has often led to the end-user perceived quality of multimedia-based services being negatively affected. Yet the quality of multimedia content is a vital issue for the continued acceptance and proliferation of these services. Indeed, end-users are becoming increasingly quality-aware in their expectations of multimedia experience and demand an ever-widening spectrum of rich multimedia-based services. As a consequence, there is a continuous and extensive research effort, by both industry and academia, to find solutions for improving the quality of multimedia content delivered to the users; as well, international standards bodies, such as the International Telecommunication Union (ITU), are renewing their effort on the standardization of multimedia technologies. There are very different directions in which research has attempted to find solutions in order to improve the quality of the rich media content delivered over various network types. It is in this context that this special issue on broadcast multimedia quality of the IEEE Transactions on Broadcasting illustrates some of these avenues and presents some of the most significant research results obtained by various teams of researchers from many countries. This special issue provides an example, albeit inevitably limited, of the richness and breath of the current research on multimedia broadcasting services. The research i- - ssues addressed in this special issue include, among others, factors that influence user perceived quality, encoding-related quality assessment and control, transmission and coverage-based solutions and objective quality measurements
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