A formal theory of plan recognition
A formal theory of plan recognition
Cyberguide: a mobile context-aware tour guide
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Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
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Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
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GeoTV: navigating geocoded rss to create an iptv experience
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Activity-based serendipitous recommendations with the Magitti mobile leisure guide
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Introduction to Information Retrieval
Introduction to Information Retrieval
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Tag recommendations based on tensor dimensionality reduction
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MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mining interesting locations and travel sequences from GPS trajectories
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Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Cross-domain activity recognition
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Collaborative prediction and ranking with non-random missing data
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Probabilistic latent preference analysis for collaborative filtering
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UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Collaborative location and activity recommendations with GPS history data
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BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Smart itinerary recommendation based on user-generated GPS trajectories
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Computing with Spatial Trajectories
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CityVoyager: an outdoor recommendation system based on user location history
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
The preface of the 4th International Workshop on Location-Based Social Networks
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Geo-activity recommendations by using improved feature combination
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Mining user similarity based on routine activities
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Location recommendation for out-of-town users in location-based social networks
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Personalized point-of-interest recommendation by mining users' preference transition
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
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Learners' acceptance of mobile technology supported collaborative learning
International Journal of Mobile Learning and Organisation
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With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Bird@?s Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the users@? location and activity histories as a user-location-activity rating tensor. Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms based on collaborative filtering. The first algorithm merges all the users@? data together, and uses a collective matrix factorization model to provide general recommendation (Zheng et al., 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (Zheng et al., 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w.r.t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations/activities for recommendations. For these three algorithms, we also exploit some additional information, such as user-user similarities, location features, activity-activity correlations and user-location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2.5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms.