Learning Early Lifecycle IV&V Quality Indicators
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Evaluating similarity measures: a large-scale study in the orkut social network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
Demographic prediction based on user's browsing behavior
Proceedings of the 16th international conference on World Wide Web
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
UM '07 Proceedings of the 11th international conference on User Modeling
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining user similarity from semantic trajectories
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A recommendation system for spots in location-based online social networks
Proceedings of the 4th Workshop on Social Network Systems
CLR: a collaborative location recommendation framework based on co-clustering
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Proceedings of the 13th international conference on Ubiquitous computing
Semantic trajectory mining for location prediction
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A Framework for Personal Mobile Commerce Pattern Mining and Prediction
IEEE Transactions on Knowledge and Data Engineering
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A HITS-based POI recommendation algorithm for location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Exploring temporal effects for location recommendation on location-based social networks
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iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOI-Mine is shown to deliver excellent performance.