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Call for Papers
Information Processing & Management Special Issue on
Personalization and Recommendation in Information Access
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* Submission deadline: September 15, 2011 *
Motivation
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The goal of enhancing IR models and methods towards user-aware and
context-aware models has raised increasing interest in the research
community, and is being identified as a key step in order to cope with
the continuous growth of information environments (repositories,
networks, users) worldwide. The notion of context refers to any dynamic
condition occurring at the time when an information retrieval task takes
place, and which may be relevant to fully define and understand a user
need. When the notion of context focuses on persistent user
characteristics and preferences, it is usually referred to as an issue
of personalization.
A significant body of research in the last two decades has paid
attention to the problem of personalizing information access and
delivery, commonly addressed under such names as information filtering,
collaborative filtering, recommender systems, or personalized IR, with
variations in approach and perspective. From different angles, the
problem has been a major research topic in fields such as IR, User
Modelling, and Machine Learning. In general, personalizing the retrieval
of content involves knowing something about the user beyond her last
request, and taking advantage of this knowledge in order to improve the
system response to the actual user need. In an increasingly demanding
and competitive market, room for such improvement exists often nowadays,
to varying degrees, in common retrieval scenarios, either because the
request is vague or because there is no explicit request at all. The
research activity in this area has been paralleled by a comparable
interest towards making such techniques commercially profitable.
The concept of Recommender System (RS) is a broader term that combines
typical features related to personalization and context. They were born
as a solution to the huge amount of information the users can find on
the Internet. RSs are applications that give advice to the user about
items (movies, music, etc.) that are likely of interest to the her,
according to her preferences and tastes. The system usually compares the
user's profile with some information extracted from the items
(content-based recommendation), or from other users who have similar
preferences (collaborative recommendation)
Personalization remains a hot topic in information access research and
industry. Important problems are yet to be solved in order to achieve
the quality, reliability and maturity required for a widespread
deployment of these techniques. Personalization systems often fail to
acquire enough or sufficiently accurate knowledge about users, as
finding implicit evidence of user needs and interests through their
behaviour is not an easy task. Inherent difficulties are involved indeed
when attempting to deal with (or even define) aspects related to human
cognition and volition. Even when the system assumptions are correct,
the adaptive actions can be obtrusive or inappropriate, if not handled
properly. Coping with the dynamics of user interests (e.g. persistent
vs. occasional), the different time scales on which they evolve (e.g.
slow persistent changes, quick transient changes), the interrelations
among different time windows (e.g. a temporal interest becoming
persistent, a long-term preference coming into play, etc.), the multiple
sides or user preferences, or the relations between preference and
situation, are some of the challenging problems in this area.
Scope
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We invite the submission of papers reporting original research, studies,
experiences, or significant advances in this area. We welcome papers
reporting theoretical, technical, experimental, and/or applicative
findings, methodological advancements, and/or contributing to the
knowledge and understanding of the field. Topics of interest include,
but are not restricted to, the following:
- Personalized information access.
- User profiling, preference elicitation and use.
- Modelling and profiling personal, social and contextual information.
- Context modelling, identification and exploitation.
- Content-based, collaborative, and hybrid recommender systems.
- Group recommendation.
- Evaluation methodologies and metrics for personalized information access.
- Temporal aspects in personalised information access.
- Practical effectiveness of personalization.
Submission
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Manuscripts shall be submitted through the Elsevier Editorial System in
the Information Processing & Management journal site, located at:
http://ees.elsevier.com/ipm/default.asp. To ensure that all manuscripts
are correctly identified for inclusion into the special issue, please
make sure you select SI: Pers & Rec in Inf Access when you reach the
-Article Type- step in the submission process.
All submissions will be reviewed by at least two specialized researchers
in the field.
Tentative schedule
- Abstract submission deadline: September 15, 2011
- Paper submission deadline: September 30, 2011
- Notification to authors: December 30, 2011
- Camera ready submission: February 28, 2012
- Publication date: TBC
Guest Editors
Juan M. Fernández-Luna ([email protected]), Universidad de Granada,
Spain
Juan F. Huete ([email protected]), Universidad de Granada, Spain
Pablo Castells ([email protected]), Universidad Autónoma de Madrid,
Spain
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Juan Manuel Fernández Luna
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Departamento de Ciencias de la Computación e Inteligencia Artificial
Escuela Técnica Superior de Ingenierías
Informática y de Telecomunicación
Universidad de Granada
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C/ Periodista Daniel Saucedo Aranda, s/n
C.P. 18071, Granada, España
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Teléfono: + 34 958 240804
Fax: + 34 958 243317
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[email protected]
http://decsai.ugr.es/~jmfluna
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