Tag recommendation for georeferenced photos
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Learning to associate relevant photos to georeferenced textual documents
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Extracting search-focused key n-grams for relevance ranking in web search
Proceedings of the fifth ACM international conference on Web search and data mining
Effective query formulation with multiple information sources
Proceedings of the fifth ACM international conference on Web search and data mining
Interactive regret minimization
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Large-scale machine learning at twitter
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Forest reranking through subtree ranking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Joshua 4.0: packing, PRO, and paraphrases
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
Proceedings of the 21st ACM international conference on Information and knowledge management
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
Training efficient tree-based models for document ranking
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Fast data in the era of big data: Twitter's real-time related query suggestion architecture
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Scaling big data mining infrastructure: the twitter experience
ACM SIGKDD Explorations Newsletter
A low rank structural large margin method for cross-modal ranking
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
About learning models with multiple query-dependent features
ACM Transactions on Information Systems (TOIS)
Fast candidate generation for real-time tweet search with bloom filter chains
ACM Transactions on Information Systems (TOIS)
Learning to rank for question routing in community question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Pairwise learning in recommendation: experiments with community recommendation on linkedin
Proceedings of the 7th ACM conference on Recommender systems
Mining search and browse logs for web search: A Survey
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Effectiveness of search result classification based on relevance feedback
Journal of Information Science
A learning approach for email conversation thread reconstruction
Journal of Information Science
The whens and hows of learning to rank for web search
Information Retrieval
Document vector representations for feature extraction in multi-stage document ranking
Information Retrieval
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Learning to rank refers to machine learning techniques for training the 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 the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing 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 SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, 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: Introduction / 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