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

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Deadline: September 15, 2011 | Date: February 28, 2012

Venue/Country: Call for Papers, U.S.A

Updated: 2011-05-11 20:38:40 (GMT+9)

Call For Papers - CFP

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 (jmflunadecsai.ugr.es), Universidad de Granada, Spain

Juan F. Huete (jhgdecsai.ugr.es), Universidad de Granada, Spain

Pablo Castells (pablo.castellsuam.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

? ? ? ? ? ? ?

jmflunadecsai.ugr.es

http://decsai.ugr.es/~jmfluna