IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Social net: using patterns of physical proximity over time to infer shared interests
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Location-Aware Information Delivery with ComMotion
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond PageRank: machine learning for static ranking
Proceedings of the 15th international conference on World Wide Web
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Linear feature-based models for information retrieval
Information Retrieval
Deciphering mobile search patterns: a study of Yahoo! mobile search queries
Proceedings of the 17th international conference on World Wide Web
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Who, what, where & when: a new approach to mobile search
Proceedings of the 13th international conference on Intelligent user interfaces
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Adapting boosting for information retrieval measures
Information Retrieval
Proceedings of the 12th ACM international conference on Ubiquitous computing
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Hyper-local, directions-based ranking of places
Proceedings of the VLDB Endowment
Learning location naming from user check-in histories
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Tips, dones and todos: uncovering user profiles in foursquare
Proceedings of the fifth ACM international conference on Web search and data mining
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
Mining web search topics with diverse spatiotemporal patterns
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A study on the accuracy of Flickr's geotag data
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
City-view image retrieval leveraging check-in data
Proceedings of the 2nd ACM international workshop on Geotagging and its applications in multimedia
Landmark-based user location inference in social media
Proceedings of the first ACM conference on Online social networks
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In this article we consider the problem of mapping a noisy estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. Despite the poor accuracy of GPS on current mobile devices and the relatively high density of places in urban areas, it is possible to predict a user's location with considerable precision by explicitly modeling both places and users and by combining a variety of signals about a user's current context. Places are often simply modeled as a single latitude and longitude when in fact they are complex entities existing in both space and time and shaped by the millions of people that interact with them. Similarly, models of users reveal complex but predictable patterns of mobility that can be exploited for this task. We propose a novel spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. We evaluate a variety of techniques and demonstrate that machine learning algorithms for ranking and spatiotemporal models of places and users offer significant improvement over common methods for location search based on distance and popularity.