640 research outputs found
The other side of the social web: A taxonomy for social information access
The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using "collective wisdom" distilled from actions of those who worked with this information earlier. This invited talk offers a brief introduction into this important research stream and reviews recent works on social information access performed at the University of Pittsburgh's PAWS Lab lead by the author. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM)
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
Preface
This special issue of the Journal on Multimodal User Interfaces collects a variety of papers dealing with multimodal corpora. Developing a multimodal corpus involves the recording, annotation and analysis of several communication modalities such as speech, hand gesture, facial expression, body posture, etc. As many research areas are moving from focused but single modality research to fully-fledged multimodality research, multimodal corpora are becoming a core research asset and an opportunity for an interdisciplinary exchange of ideas, concepts and data. The number of publicly available multimodal corpora is constantly growing as is the interest in studying multimodal communication by machines and through machines. This is a very positive trend, since the availability of large annotated multimodal corpora in different application domains, and for different languages, is a necessary prerequisite for the development of innovative, intelligent and flexible multimodal user interfaces.peer-reviewe
A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems
Personalisation, adaptation and recommendation are central features
of TEL environments. In this context, information retrieval techniques are applied
as part of TEL recommender systems to filter and recommend learning resources
or peer learners according to user preferences and requirements. However,
the suitability and scope of possible recommendations is fundamentally
dependent on the quality and quantity of available data, for instance, metadata
about TEL resources as well as users. On the other hand, throughout the last
years, the Linked Data (LD) movement has succeeded to provide a vast body of
well-interlinked and publicly accessible Web data. This in particular includes
Linked Data of explicit or implicit educational nature. The potential of LD to
facilitate TEL recommender systems research and practice is discussed in this
paper. In particular, an overview of most relevant LD sources and techniques is
provided, together with a discussion of their potential for the TEL domain in
general and TEL recommender systems in particular. Results from highly related
European projects are presented and discussed together with an analysis of
prevailing challenges and preliminary solutions.LinkedU
Adaptive Information Visualization for Personalized Access to Educational Digital Libraries
Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Panorama of Recommender Systems to Support Learning
This chapter presents an analysis of recommender systems in TechnologyEnhanced
Learning along their 15 years existence (2000-2014). All recommender
systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from
35 different countries have been investigated and categorised according to a given
classification framework. The reviewed systems have been classified into 7 clusters
according to their characteristics and analysed for their contribution to the evolution
of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424).
Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders
(FWO). Olga C. Santos would like to acknowledge that her contributions to this
work have been carried out within the project Multimodal approaches for Affective
Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts
(MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported
with funding CIP-PSP Open Discovery Space (297229
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