Hang Li

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

Second Edition

Learning to Rank for Information Retrieval and Natural Language Processing

Wei-Ying Ma

Ting Liu

Tetsuya Sakai

Kam-Fai Wong

Guodong Zhou

AsiaInformationRetrievalSymposium(AIRS)2008wasthefourthAIRSconf- ence in the series established in 2004.The ?rst AIRS washeld in Beijing, China, the second in Jeju, Korea, and the third in Singapore. The AIRS conferences trace their roots to the successful Information Retrieval with Asian Languages (IRAL) workshops, which started in 1996. The AIRS series aims to bring together international researchers and dev- opers to exchange new ideas and the latest results in information retrieval. The scope of the conference encompasses the theory and practice of all aspects of information retrieval in text, audio, image, video, and multimedia data. We are pleased to report that AIRS 2006 receiveda largenumber of 144 s- missions. Submissions came from all continents: Asia, Europe, North America, South America and Africa. We accepted 39 submissions as regular papers (27%) and 45 as short papers (31%). All submissions underwent double-blind revi- ing. We aregratefulto all the area Co-chairswho managedthe review processof their respective area e?ciently, as well as to all the Program Committee m- bers and additional reviewers for their e?orts to get reviews in on time despite the tight time schedule. We are pleased that the proceedings are published by Springer as part of their Lecture Notes in Computer Science (LNCS) series and that the papers are EI-indexed.

4th Asia Information Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008, Revised Selected Papers

Information Retrieval Technology

This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China in May 2007.The 34 revised full papers and 92 revised short papers presented together with 4 keynote talks or extended abstracts thereof were carefully reviewed and selected from 730 submissions. The papers are devoted to new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, databases, statistics, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.

11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007, Proceedings

Advances in Knowledge Discovery and Data Mining

Hang Li

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