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--------------------------- Call for Papers ---------------------------

ACM Transactions on Intelligent Systems and Technology (ACM TIST)

Special Issue on

Diversityand Discovery in Recommender Systems and Exploratory Search

http://tist.acm.org/CFPs/TIST-SI-DIVERS.pdf

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• Submission deadline: 20 September 2012 *

Overview

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Most research and development efforts in information access technologies have been focused on the accuracy in predicting and matching user information needs. Moreover, accuracy has been defined as a property that considers individual delivered items in isolation, or even more narrowly, as the ability to reduce the errors between predicted user preference values and explicit user votes. However there is a growing realization that there is more than accuracy to the practical effectiveness and added-value of delivered information. In particular,diversityand discovery have been identified as key dimensions of information utility in real scenarios, and a fundamental research direction to keep making progress in the field. The importance of novelty anddiversityis particularly manifest in scenarios such as automatic recommendation and exploratory search, in which user needs involve some degree of uncertainty and/or underspecification, leaving room for the systems initiative to complete and predict these needs on behalf of –or in collaboration with– the user.

The intelligence to discover something novel is indeed essential to a recommender system: in many, if not most scenarios, the whole point of recommendation is inherently linked to the provision of discovery, as recommendation makes most sense when it exposes the user to a relevant experience that she would not have found, or thought of by herself –obvious, however accurate, recommendations are generally of little use. Not only does a varied recommendation provide in itself for a richer user experience. Given the inherent uncertainty in user interest prediction –since it is based on implicit, incomplete evidence of past interests, where the latter are moreover subject to change– avoiding a too narrow array of choice is generally a good approach to enhance the chances that the user is pleased by at least some recommended item. Salesdiversitymay enhance businesses as well, leveraging revenues from market niches.

The value ofdiversityhas likewise been acknowledged in exploratory retrieval scenarios where information needs have a specific orientation, but involve a degree of flexibility, generality and open-endedness comparable to user needs in recommendation tasks. Discovery is indeed a fundamental drive –and an inherent part of user goals– in informational and exploratory search, where new theories and methods are being developed that model novelty and relevance as two sides of the same coin.

Diversityand discovery represent a cutting-edge research area in intelligent information access and delivery, eliciting increasing attention from researchers and practitioners in the recommender systems, information retrieval, and machine learning communities. This special issue seeks to gather a selection of papers reporting leading research ondiversityand discovery perspectives in recommender systems and exploratory search, providing a view of the latest advances in this scope.

Topics of interest

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We invite the submission of high-quality manuscripts reporting relevant research in the area ofdiversity, discovery, and novelty in recommender systems and exploratory search. The special issue welcomes submissions presenting theoretical, technical, experimental, methodological and/or applicative contributions in this scope, addressing –though not limited to– the following topics:

*Diversityand discovery modeling in recommender systems

• Theoretical foundation for novelty anddiversity

• Recommendation novelty anddiversitymodels

• Popularity, risk, unexpectedness, surprisal, serendipity, freshness

• Link todiversitymodels in Information Retrieval

*Diversityand discovery enhancement in recommender systems

• Diversification methods

• Recommendation of long-tail and difficult items, cold-start problem

• Individual vs. globaldiversity

• Machine Learning fordiversityand discovery

*Diversityand discovery across recommendations

-Diversityand discovery in sequential recommendation

-Diversityand discovery in interactive recommendation

• Aggregatediversity

-Diversityand discovery in time and context

• Novelty and trust

*Diversityand discovery in exploratory search

• Novelty anddiversityin informational search and exploratory browsing

-Diversityand discovery in interactive search

• Novelty,diversityand personalization

• Novelty, learning and knowledge gain

*Diversityand discovery evaluation

• Experimental methodologies and design

• Novelty anddiversitymetrics

• Datasets

• User studies

• Applications

• Discovery-oriented domains, applications, and scenarios

• Business perspective ondiversityand discovery

• Domain-specific studies

Submissions

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Manuscripts shall be sent through the ACM TIST electronic submission system athttp://mc.manuscriptcentral.com/tist(please select "Special Issue:Diversityand Discovery in Recommender Systems and Exploratory Search" as the manuscript type). Submissions shall adhere to the ACM TIST instructions and guidelines for authors available at the journal web site:http://tist.acm.org.

Each paper will be evaluated by at least two reviewers. The papers will be evaluated for their originality, contribution significance, soundness, clarity, and overall quality. The interest of contributions will be assessed in terms of technical and scientific findings, contribution to the knowledge and understanding of the problem, methodological advancements, and/or applicative value.

Important Dates

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• Deadline for submission: 20 September 2012

• Review notification: 15 November 2012

• Revised manuscript due: 20 December 2012

Guest Editors

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Pablo Castells, Universidad Autonoma de Madrid

Jun Wang, University College London

Ruben Lara, Telefonica, Investigacion y Desarrollo

Dell Zhang, Birkbeck, University of London