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Introduction and topicsIn recent years, several methods for personalizing information offerings have been developed to combat the information overload caused by the vast range and continued growth of content available on the World Wide Web. Examples include adaptive hypertext, which adapts the user interface to the users’ interests and needs (e.g., in mobile portals), and recommender systems that try to predict what items, e.g., news, movies or travels, would be relevant for the user. Recommender systems use various AI and IR techniques to build their predictions and typically interact with the user. Many of these techniques and systems have made their way from research into commercial applications and are now widely used in major eCommerce portals. At the same time, new techniques are being proposed, for improving the prediction accuracy or offering new ways for users to participate, as in social networks in Web 2.0 platforms.Choosing the right evaluation method for personalized web applications, identifying the influential success factors behind different techniques or interpreting results coming from online experiments remain largely open research issues. Recommender systems traditionally have been evaluated with off-line experiments trying to estimate the recommendation prediction error, using an existing dataset of recommendations. In fact, many researchers have criticised the limitations of such methods, to the point that some have argued that the true accuracy of a recommender system could be never directly measured. But the widespread use of recommender systems makes it crucial to develop methods to realistically and accurately assess their true performances and effect on the users. For these reasons, this special issue seeks to foster scientific work on understanding how personalization and recommendation impact user expectations, beliefs and behaviour during and after the interaction. Additionally, it aims to provide a timely review of research on this topic. The special issue is concerned not only with the effects on individual users but also with the marketing and social aspects of personalized content and personalized product offerings towards online customers. Thus, we are calling for contributions addressing general as well as more specific research questions: What are the shortcomings of most of current evaluation studies and methods? Do personalization applications in different domains pursue different goals? Which assessment criteria and evaluation methodologies can be applied? How do specific aspects of recommendation systems such as online conversations, preference elicitation strategies or explanations impact online users? Can personalized online interactions persuade users to buy a product or service? What quantitative benefits do businesses obtain by personalizing their online presences? Two kinds of submissions are encouraged:
Possible topics include, but are not limited to:
Downloads and further informationPaper submissionAny questions and one page abstracts should be directed to impact_ijhcs@ifit.uni-klu.ac.at. Manuscripts should not exceed 8000 words and papers should be submitted according to the IJHCS Guide for authors and will be refereed in the standard way. Articles must be based on original research, although extended versions of conference papers may be acceptable if they contain at least 50% new material. All manuscripts should be submitted online. The IJHCS Guide for authors and online submission is available at http://ees.elsevier.com/ijhcs. To submit to the special issue, please select Article Type “SI: Impact Personalization” and clearly state in the “Enter Comments” section that the paper is intended for the “Special Issue on Measuring the Impact of Personalization and Recommendation on User Behaviour”. If you are a first time user of the journal’s online submission tool, you will have to register yourself as an author on the system. If you have any problems with the system contact Fred Kop, Journal Manager, at ijhcs@elsevier.com.TimelineAbstract submission:Notification on abstracts: 15 Oct. 2008 Submissions due to review: 30 Dec. 2008 (at http://ees.elsevier.com/ijhcs) Notification of 1st review: 30 Apr. 2009 Revised versions due: 15 July 2009 Final notification: 15 Oct. 2009 Final revisions due: 15 Nov. 2009 Target publication date: March/April 2010 Guest editorsMarkus Zanker, University Klagenfurt, AustriaFrancesco Ricci, Free University of Bolzano/Bozen, Italy Loren Terveen, University of Minnesota, USA Dietmar Jannach Dortmund University of Technology, Germany Contact and inquiriesMail to editorsJournal homepage |
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