Min Zhang

Fabrizio Silvestri

Tie-Yan Liu

Rafael Banchs

Sheng Gao

Jun Lang

This book constitutes the refereed proceedings of the 9th Information Retrieval Societies Conference, AIRS 2013, held in Singapore, in December 2013. The 27 full papers and 18 poster presentations included in this volume were carefully reviewed and selected from 109 submissions. They are organized in the following topical sections: IR theory, modeling and query processing; clustering, classification and detection; natural language processing for IR; social networks, user-centered studies and personalization and applications.

9th Asia Information Retrieval Societies Conference, AIRS 2013, Singapore, December 9-11, 2013, Proceedings

Information Retrieval Technology

Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.

Learning to Rank for Information Retrieval

Christie Napa Scollon

Wenwu Zhu

This book constitutes the proceedings of the 7th International Conference on Social Informatics, SocInfo 2015, held in Beijing, China, in December 2015. The 19 papers presented in this volume were carefully reviewed and selected from 42 submissions. They cover topics such as user modeling, opinion mining, user behavior, and crowd sourcing.

7th International Conference, SocInfo 2015, Beijing, China, December 9-12, 2015, Proceedings

Social Informatics

Tie-Yan Liu

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