2012 - Information Processing & Management Special Issue on Personalization and Recommendation in Information Access

Information Processing & Management Special Issue on
Personalization and Recommendation in Information Access


* Submission deadline: September 15, 2011 *

Motivation


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


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


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 (jmfluna-AT-decsai.ugr.es), Universidad de Granada, Spain
Juan F. Huete (jhg-AT-decsai.ugr.es), Universidad de Granada, Spain
Pablo Castells (pablo.castells-AT-uam.es), Universidad Autónoma de Madrid, Spain


? ? ? ? ? ? ? ? ? ? Juan Manuel Fernández Luna

Departamento de Ciencias de la Computación e Inteligencia Artificial
? ? ? ? ? ? ? ?Escuela Técnica Superior de Ingenierías
? ? ? ? ? ? ? ? ?Informática y de Telecomunicación
? ? ? ? ? ? ? ? ? ? ? Universidad de Granada

? ? ? ? ? ? ? C/ Periodista Daniel Saucedo Aranda, s/n
? ? ? ? ? ? ? ? ? ? ? C.P. 18071, Granada, España
? ? ? ? ? ? ?
? ? ? ? ? ? ? ? ? ? ?Teléfono: + 34 958 240804
? ? ? ? ? ? ? ? ? ? ?Fax: ? ? ?+ 34 958 243317
? ? ? ? ? ? ?
jmfluna-AT-decsai.ugr.es
http://decsai.ugr.es/~jmfluna