http://www.bibsonomy.org/burst/user/flint63/adaptiveBibSonomy BuRST Feed for /user/flint63/adaptive2008-05-16T18:57:06+02:00http://www.bibsonomy.org/bibtex/25868e11c46916602925fa6bdf8783aef/flint63flint632008-05-03T17:09:17+02:00adaptive ai book design information interaction interface management requirements semantic springer user v0804 web <span style="color:#555555;">Peter <a href="http://www.bibsonomy.org/author/Brusilovsky">Brusilovsky</a> und Alfred <a href="http://www.bibsonomy.org/author/Kobsa">Kobsa</a> und Wolfgang <a href="http://www.bibsonomy.org/author/Nejdl">Nejdl</a> (eds.). </span><em>Lecture Notes in Computer Science </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Berlin, HeidelbergLecture Notes in Computer ScienceThe Adaptive Web: Methods and Strategies of Web Personalization43212007adaptiveaibookdesigninformationinteractioninterfacemanagementrequirementssemanticspringeruserv0804webWeb personalization has evolved into a large research field that attracts scientists from different communities such as hypertext, user modeling, machine learning, natural language generation, information retrieval, intelligent tutoring systems, cognitive science, and Web-based education. This state-of-the-art survey provides a systematic overview of the ideas and techniques of the adaptive Web and serves as a central source of information for researchers, practitioners, and students. The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field. The largest part of the book focuses on personalization techniques, namely the modeling side of personalization and on adaptation. This technique-focused part is complemented by four domain-oriented chapters. The book also details recently emerging topics; it provides a prospective view to new ideas and techniques that are moving rapidly into the focus of the adaptive Web community and have to be included as a glimpse into a not so distant future.http://www.bibsonomy.org/bibtex/296abfa5b8e09fde21a1faa654ec47d6a/flint63flint632008-05-03T17:09:17+02:00adaptive ai community interaction interface knowledge management paper semantic software springer user v0804 web <span style="color:#555555;">Amy <a href="http://www.bibsonomy.org/author/Soller">Soller</a> </span><em>The Adaptive Web: Methods and Strategies of Web Personalization, </em><em>Volume4321vonLecture Notes in Computer Science, </em><em>chapter 19, </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization19573-595Lecture Notes in Computer ScienceAdaptive Support for Distributed Collaboration43212007adaptiveaicommunityinteractioninterfaceknowledgemanagementpapersemanticsoftwarespringeruserv0804webThrough interaction with others, a person develops multiple perspectives that become the basis for innovation and the construction of new knowledge. This chapter discusses the challenges facing emerging web-based technologies that enable distributed users to discover and construct new knowledge collaboratively. Examples include advanced collaborative and social information filtering technology that not only helps users discover knowledge, peers, and relevant communities, but also plays a powerful role in facilitating and mediating their interaction. As the internet extends around the world and interconnects diverse cultures, the adaptive web will be challenged to provide a personalized knowledge interface that carries new perspectives to diverse communities. It will play the role of an interface for knowledge construction, a mediator for communication and understanding, and a structured channel through which knowledge is created, interpreted, used, and recreated by other users.http://www.bibsonomy.org/bibtex/2713898eab2c82bd5857819fc02d4030e/flint63flint632008-05-03T17:09:17+02:00adaptive ai assist information management mobile paper portal v0804 web <span style="color:#555555;"> </span><em>AI Magazine</em><em>29(1):29-40</em>(<em>2008</em>)AI Magazine129-40Intelligent Content Discovery on the Mobile Internet: Experiences and Lessons Learned292008adaptiveaiassistinformationmanagementmobilepaperportalv0804webThe mobile Internet represents a massive opportunity for mobile operators and content providers. Today there are more than 2 billion mobile subscribers, with 3 billion predicted by the end of 2007. However, despite significant improvements in handsets, infrastructure, content, and charging models, mobile users are still struggling to access and locate relevant content and services. An important part of this so-called content-discovery problem relates to the navigation effort that users must invest in browsing and searching for mobile content. In this article we describe one successfully deployed solution, which uses personalization technology to profile subscriber interests in order to automatically adapt mobile portals to their learned preferences. We present summary results, from our deployment experiences with more than 40 mobile operators and millions of subscribers around the world, which demonstrate how this solution can have a significant impact on portal usability, subscriber usage, and mobile operator revenues.http://www.bibsonomy.org/bibtex/23d7f5e7bb7db1f77615e737a50a8e8a7/flint63flint632008-05-03T17:09:17+02:00adaptive ai assist information interaction interface management paper requirements springer user v0804 web <span style="color:#555555;">Barry <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>The Adaptive Web: Methods and Strategies of Web Personalization, </em><em>Volume4321vonLecture Notes in Computer Science, </em><em>chapter 11, </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization11342-376Lecture Notes in Computer ScienceCase-Based Recommendation43212007adaptiveaiassistinformationinteractioninterfacemanagementpaperrequirementsspringeruserv0804webRecommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems.http://www.bibsonomy.org/bibtex/2dddb0ddc7d53e1d03a2d21bb71c22f63/flint63flint632008-05-03T17:09:17+02:00adaptive ai application book community data development knowledge management pattern python recognition software user v0804 web <span style="color:#555555;">Toby <a href="http://www.bibsonomy.org/author/Segaran">Segaran</a> </span><em>O'Reilly, </em><em>K&#246;ln, </em>(<em>2008</em>)Köln{Kollektive Intelligenz analysieren, programmieren und nutzen}2008adaptiveaiapplicationbookcommunitydatadevelopmentknowledgemanagementpatternpythonrecognitionsoftwareuserv0804webSie wollen wissen, wie Rankings, Produktempfehlungen, Social Bookmarking und Online-Partnerbörsen technisch funktionieren? Dieses faszinierende Buch zeigt Ihnen, wie Sie Web 2.0-Applikationen bauen, mit denen Sie die riesigen Datenmengen durchsuchen und analysieren können, die von den Benutzern aktueller Webanwendungen täglich erzeugt werden. Es nimmt Sie mit in die Welt des maschinellen Lernens und der Statistik und erklärt, wie Sie Schlussfolgerungen aus User Experience, persönlichen Vorlieben und menschlichem Verhalten ziehen. Mit den ausgereiften Algorithmen in diesem Buch können Sie raffinierte Programme schreiben, mit denen Sie auf die &#034;kollektive Intelligenz&#034; der Website-Nutzer zugreifen können. Behandelt werden beispielsweise kollaborative Filtertechniken, Cluster-Methoden, Optimierungs-Algorithmen, das Bayes&#039;sche Filtern oder Support-Vektor-Maschinen. Jeder Algorithmus ist kurz und prägnant durch Code beschrieben, den Sie direkt für Ihr Website-Projekt nutzen können.http://www.bibsonomy.org/bibtex/28719535a1e41f0e09be8d18063ac0707/flint63flint632008-05-03T17:09:17+02:00adaptive ai application book community data development knowledge management pattern python recognition software user v0804 web <span style="color:#555555;">Toby <a href="http://www.bibsonomy.org/author/Segaran">Segaran</a> </span><em>O'Reilly, </em><em>Sebastopol, CA, USA, </em>(<em>2007</em>)Sebastopol, CA, USAProgramming Collective Intelligence: Building Smart Web 2.0 Applications2007adaptiveaiapplicationbookcommunitydatadevelopmentknowledgemanagementpatternpythonrecognitionsoftwareuserv0804webWant to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build web applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you&#039;ve found it.http://www.bibsonomy.org/bibtex/21c611c2e32fb3b735c3adcd413e95201/flint63flint632008-05-03T17:09:17+02:00adaptive ai application assist community information management paper springer user v0804 web <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Ben Schafer">Ben Schafer</a> und Dan <a href="http://www.bibsonomy.org/author/Frankowski">Frankowski</a> und Jon <a href="http://www.bibsonomy.org/author/Herlocker">Herlocker</a> und Shilad <a href="http://www.bibsonomy.org/author/Sen">Sen</a> </span><em>The Adaptive Web: Methods and Strategies of Web Personalization, </em><em>Volume4321vonLecture Notes in Computer Science, </em><em>chapter 9, </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization9291-324Lecture Notes in Computer ScienceCollaborative Filtering Recommender Systems43212007adaptiveaiapplicationassistcommunityinformationmanagementpaperspringeruserv0804webOne of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.http://www.bibsonomy.org/bibtex/2f516683abff006b44726a3016bfaea63/flint63flint632008-05-03T17:09:17+02:00adaptive crisis data ieee management paper secure v0804 <span style="color:#555555;">Arjmand <a href="http://www.bibsonomy.org/author/Samuel">Samuel</a> und Arif <a href="http://www.bibsonomy.org/author/Ghafoor">Ghafoor</a> und Elisa <a href="http://www.bibsonomy.org/author/Bertino">Bertino</a> </span><em>IEEE Internet Computing</em><em>12(1):51-54</em>(<em>2008</em>)IEEE Internet Computing151-54Context-Aware Adaptation of Access-Control Policies122008adaptivecrisisdataieeemanagementpapersecurev0804Today, public-service delivery mechanisms such as hospitals, police, and fire departments rely on digital generation, storage, and analysis of vital information. To protect critical digital resources, these organizations employ access-control mechanisms, which define rules under which authorized users can access the resources they need to perform organizational tasks. Natural or man-made disasters pose a unique challenge, whereby previously defined constraints can potentially debilitate an organization&#039;s ability to act. Here, the authors propose employing contextual parameters---specifically, activity context in the form of emergency warnings---to adapt access-control policies according to a priori configuration.http://www.bibsonomy.org/bibtex/24fe3dfb6bc57a4d96445b24623786d61/flint63flint632008-05-03T17:09:17+02:00adaptive ai application assist information interface management paper requirements springer user v0804 web <span style="color:#555555;">Michael J. <a href="http://www.bibsonomy.org/author/Pazzani">Pazzani</a> und Daniel <a href="http://www.bibsonomy.org/author/Billsus">Billsus</a> </span><em>The Adaptive Web: Methods and Strategies of Web Personalization, </em><em>Volume4321vonLecture Notes in Computer Science, </em><em>chapter 10, </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization10325-341Lecture Notes in Computer ScienceContent-Based Recommendation Systems43212007adaptiveaiapplicationassistinformationinterfacemanagementpaperrequirementsspringeruserv0804webThis chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.http://www.bibsonomy.org/bibtex/26fee4ba718181dc1d09ccc02a704661d/flint63flint632008-05-03T17:09:17+02:00adaptive ai data paper pattern recognition springer user v0804 web <span style="color:#555555;">Bamshad <a href="http://www.bibsonomy.org/author/Mobasher">Mobasher</a> </span><em>The Adaptive Web: Methods and Strategies of Web Personalization, </em><em>Volume4321vonLecture Notes in Computer Science, </em><em>chapter 3, </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2007</em>)Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization390-135Lecture Notes in Computer ScienceData Mining for Web Personalization43212007adaptiveaidatapaperpatternrecognitionspringeruserv0804webIn this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and pre-processing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.