Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

9.2 Future Directions 191to show the procedures of capturing the underlying navigation tasks and forming theuser access patterns from Web logs via probability inference approaches. The combinationof clustering and latent semantic analysis is fully addressed as well. Then anumber of Web usage mining applications are reviewed in this chapter to emphasizethe application potentials along with some experimental studies.The latter mission of this book is reflected in Chapter.7 and Chapter.8, where tworecently active and popular topics - social networks and Web recommendation, arecovered. Following the basic backgrounds discussed in Chap.2 and Chap.3, Chapter7 concentrates mainly on a few important technical issues of social networking.It presents some algorithms with respect to detecting, extracting and analyzing theWeb community structures and social networks and their dynamic evolutions by usingWeb data mining. We discuss the approach of using Web archive and graph tocapture the Web community and its evolution, which is based on graph mining andWeb community discovery approaches. In addition, we report the studies of temporalanalysis of Web networked structures using three-way tensor analysis. The additionaldimension of temporal feature makes it possible to capture the dynamic change ofnetworked structures at a high level of spatial-temporal space. The reported work oncombining social network discovery and evolution analysis provides an instructivehint in a unified fashion of social network analysis. We aim to present the materialsof social network analysis from the perspectives of longitudinal, evolutionary andunified aspects. Apart from the algorithmic researches, we also give an real worldstudy of social network analysis in the context of societal and social behavior ine-commerce. Chapter 8 talks about the topic of Web recommendation. We aim topresent materials following a thread of Web data mining. After introducing algorithmsand techniques of the traditional recommender systems, such as user-base,item-based and a hybrid recommender systems, we aim to illustrate how Web miningis able to help improve the recommendation. Especially we report several studiesin Web recommendation via Web mining. In usage-based user profiling approaches,we discuss the algorithmic descriptions on using the user access patterns derivedwith Web usage mining to predict the users’ more interested contents. We also reviewthe study of combining Web archives and logs (i.e. the combination of Webcontent and usage mining) for Web query recommendation. With th potential capabilityof Web recommendation in improving user satisfaction and enterprise marketvalue, this chapter prepares a progressive landscaping of the start-of-the-art Web recommendation.Next, we will outline some future research directions in the area ofWeb mining and social networking, focusing on the issues of combination of thesetwo aspects, and social media and social network computing because it attracts alarge volume of attentions from various disciplines.9.2 Future DirectionsWith the coming era of Web 2.0 and propagation of related technologies and applications,social media mining and social network computing is becoming an activeinterdisciplinary area, which attracts attention from different research areas, such