Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the 12th ACM international conference on Ubiquitous computing
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
When recommendation meets mobile: contextual and personalized recommendation on the go
Proceedings of the 13th international conference on Ubiquitous computing
City-view image retrieval leveraging check-in data
Proceedings of the 2nd ACM international workshop on Geotagging and its applications in multimedia
Geographical and temporal similarity measurement in location-based social networks
Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Where you like to go next: successive point-of-interest recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals. While existing research predominantly focuses on one-step recommendation---recommending the next single activity according to current context, this work moves one step beyond by recommending a series of activities, which is a package of sequential Points of Interest (POIs). The recommended POIs are not only relevant to user context (i.e., current location, time, and check-in), but also personalized to his/her check-in history. We presents a probabilistic approach, which is highly motivated from a large-scale commercial mobile check-in data analysis, to ranking a list of sequential POI categories (e.g., "Japanese food" and "bar") and POIs (e.g., "I love sushi"). The approach enables users to plan consecutive activities on the move. Specifically, the probabilistic recommendation approach estimates the transition probability from one POI to another, conditioned on current context and check-in history in a Markov chain. To alleviate the discritization error and sparsity problem, we further introduce context collaboration and integrate prior information. Experiments on over 100k real-world check-in records and 20k POIs validate the effectiveness of the proposed approach.