Bamshad Mobasher – författare

Web personalizationcan be de?ned as any set of actions that can tailor the Web experience to a particular user or set of users. The experience can be something as casualas browsinga Web site oras (economically)signi?cantas tradingstock or purchasing a car. The actions can range from simply making the presentation more pleasing to anticipating the needs of a user and providing customized and relevant information. To achieve e?ective personalization, organizations must rely on all available data, including the usage and click-stream data (re?e- ing user behavior), the site content, the site structure, domain knowledge, user demographics and pro?les. In addition, e?cient and intelligent techniques are needed to mine these data for actionable knowledge, and to e?ectively use the discovered knowledge to enhance the users' Web experience. These techniques must address important challenges emanating from the size and the heteroge- ity of the data, and the dynamic nature of user interactions with the Web. E-commerce and Web information systems are rich sources of di?cult pr- lems and challenges for AI researchers.These challenges include the scalability of the personalization solutions, data integration, and successful integration of techniques from machine learning, information retrievaland ?ltering, databases, agent architectures, knowledge representation, data mining, text mining, stat- tics, user modelling and human-computer interaction. Throughout the history of the Web, AI has continued to play an essential role in the development of Web-based information systems, and now it is believed that personalization will prove to be the "killer-app" for AI.

TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.

TheWebisaliveenvironmentthatmanagesanddrivesawidespectrumofapp- cations in which a user may interact with a company, a governmental authority, a non-governmental organization or other non-pro?t institution or other users. User preferences and expectations, together with usage patterns, form the basis for personalized, user-friendly and business-optimal services. Key Web business metrics enabled by proper data capture and processing are essential to run an e?ective business or service. Enabling technologies include data mining, sc- able warehousing and preprocessing, sequence discovery, real time processing, document classi?cation, user modeling and quality evaluation models for them. Recipient technologies required for user pro?ling and usage patterns include recommendation systems, Web analytics applications, and application servers, coupled with content management systems and fraud detectors. Furthermore, the inherent and increasing heterogeneity of the Web has - quired Web-based applications to more e?ectively integrate a variety of types of data across multiple channels and from di?erent sources. The development and application of Web mining techniques in the context of Web content, Web usage, and Web structure data has already resulted in dramatic improvements in a variety of Web applications, from search engines, Web agents, and content management systems, to Web analytics and personalization services. A focus on techniques and architectures for more e?ective integration and mining of c- tent, usage,and structure data from di?erent sourcesis likely to leadto the next generation of more useful and more intelligent applications.

This book contains the postworkshop proceedings with selected revised papers from the 8th international workshop on knowledge discovery from the Web, WEBKDD 2006. The WEBKDD workshop series has taken place as part of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto the Web.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interaction and business has led to new challenges and to intensive, dedicated research.Many ofthe infancy problems in Web mining have been solvedby now, but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges. ThethemeoftheWebKDD2006workshopwas“KnowledgeDiscoveryonthe Web”, encompassing lessons learned over the past few years and new challenges for the years to come. While some of the infancy problems of Web analysis have beensolvedandproposedmethodologieshavereachedmaturity,therealityposes newchallenges:TheWebisevolvingconstantly;siteschangeanduserpreferences drift. And, most of all, a Web site is more than a see-and-click medium; it is a venue where a user interacts with a site owner or with other users, where group behavior is exhibited, communities are formed and experiences are shared.

This book contains the postworkshop proceedings with selected revised papers from the 8th international workshop on knowledge discovery from the Web, WEBKDD 2006. The WEBKDD workshop series has taken place as part of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto the Web.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interaction and business has led to new challenges and to intensive, dedicated research.Many ofthe infancy problems in Web mining have been solvedby now, but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges. ThethemeoftheWebKDD2006workshopwas“KnowledgeDiscoveryonthe Web”, encompassing lessons learned over the past few years and new challenges for the years to come. While some of the infancy problems of Web analysis have beensolvedandproposedmethodologieshavereachedmaturity,therealityposes newchallenges:TheWebisevolvingconstantly;siteschangeanduserpreferences drift. And, most of all, a Web site is more than a see-and-click medium; it is a venue where a user interacts with a site owner or with other users, where group behavior is exhibited, communities are formed and experiences are shared.

Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.

Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.

This book constitutes the refereed proceedings of the 20 th International Conference on User Modeling, Adaptation, and Personalization, held in Montreal, Canada, in July 2012. The 22 long and 7 short papers of the Research Paper Track presented were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on user engagement; trust; user motivation, attention, and effort; recommender systems (including topics such as matrix factorization, critiquing, noise and spam in recommender systems); user centered design and evaluation; educational data mining; modeling learners; user models in microblogging; and visualization. The Industry Paper Track covered innovative commercial implementations or applications of UMAP technologies, and experience in applying recent research advances in practice. 2 long and 1 short papers were accepted of 5 submissions.