Guandong Xu – författare

This book examines the techniques and applications involved in the Web Mining, Web Personalization and Recommendation and Web Community Analysis domains, including a detailed presentation of the principles, developed algorithms, and systems of the research in these areas. The applications of web mining, and the issue of how to incorporate web mining into web personalization and recommendation systems are also reviewed. Additionally, the volume explores web community mining and analysis to find the structural, organizational and temporal developments of web communities and reveal the societal sense of individuals or communities.

The volume will benefit both academic and industry communities interested in the techniques and applications of web search, web data management, web mining and web knowledge discovery, as well as web community and social network analysis.

Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users’ past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View Prediction describes new approaches for tackling this problem by utilizing users’ trust relationships from the perspectives of fundamental theory, trust-based collective view prediction algorithms and real case studies.

The book consists of two main parts – a theoretical foundation and an algorithmic study. The first part will review several basic concepts and methods related to collective view prediction, such as state-of-the-art recommender systems, sentimental analysis, collective view, trust management, the Relationship of Collective View and Trustworthy, and trust in collective view prediction. In the second part, the authors present their models and algorithms based on a quantitative analysis of more than 300 thousand users’ data from popular product-reviewing websites. They also introduce two new trust-based prediction algorithms, one collaborative algorithm based on the second-order Markov random walk model, and one Bayesian fitting model for combining multiple predictors.

The discussed concepts, developed algorithms, empirical results, evaluation methodologies and the robust analysis framework described in Trust-based Collective View Prediction will not only provide valuable insights and findings to related research communities and peers, but also showcase the great potential to encourage industries and business partners tointegrate these techniques into new applications.

This book constitutes the Revised Selected Papers of the Third International Conference on Artificial Intelligence and Knowledge Processing, AIKP 2023, held in Hyderabad, India, during October 6–8, 2023.

The 20 full papers and 8 short papers were carefully selected from 118 submissions. The research areas include: Artificial Intelligence and Machine Learning; Deep Learning and Computer Vision; Natural Language Processing; Intelligent Control.

This book constitutes the thoroughly refereed joint post-workshop proceedings of three international workshops held in conjunction with the 10th Asia-Pacific Web Conference, APWeb 2008, in Shenyang, China, in April 2008 (see LNCS 4976).

The 15 revised full papers presented together with 4 invited papers and 4 keynote lectures were carefully reviewed and selected from numerous submissions. Topics addressed by the workshops are business intelligence and data mining (BIDM 2008), health data management (IWHDM 2008), and data engineering and Web technology research (DeWeb 2008). The papers focus on issues such as Web searching, Web services, database, data mining, bioinformatics, and business intelligence.