Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Convex Optimization
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Proceedings of the 16th international conference on World Wide Web
Retrieval and feedback models for blog feed search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Discovering users' specific geo intention in web search
Proceedings of the 18th international conference on World wide web
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Model adaptation via model interpolation and boosting for web search ranking
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Ranking mechanisms in twitter-like forums
Proceedings of the third ACM international conference on Web search and data mining
Interactive retrieval based on faceted feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Personalize web search results with user's location
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Hyper-local, directions-based ranking of places
Proceedings of the VLDB Endowment
Learning to rank with multiple objective functions
Proceedings of the 20th international conference on World wide web
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Predicting primary categories of business listings for local search
Proceedings of the 21st ACM international conference on Information and knowledge management
Search result presentation: supporting post-search navigation by integration of taxonomy data
Proceedings of the 22nd international conference on World Wide Web companion
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Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects (e.g., text matching and distance in local search). Thus the overall relevance between a query and a document is a tradeoff between multiple relevance aspects. Such a tradeoff can vary for different types of queries or in different contexts. In this paper, we explore these vertical-specific aspects in the learning to rank setting. We propose a novel formulation in which the relevance between a query and a document is assessed with respect to each aspect, forming the multi-aspect relevance. In order to compute a ranking function, we study two types of learning-based approaches to estimate the tradeoff between these relevance aspects: a label aggregation method and a model aggregation method. Since there are only a few aspects, a minimal amount of training data is needed to learn the tradeoff. We conduct both offline and online test experiments on a local search engine and the experimental results show that our proposed multi-aspect relevance formulation is very promising. The two types of aggregation methods perform more effectively than a set of baseline methods including a conventional learning to rank method.