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
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An efficient method for mining associated service patterns in mobile web environments
Proceedings of the 2003 ACM symposium on Applied computing
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Mining Cluster-Based Mobile Sequential Patterns in Location-Based Service Environments
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Probabilistic latent semantic user segmentation for behavioral targeted advertising
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Discovering significant places from mobile phones: a mass market solution
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Proceedings of the fourth ACM conference on Recommender systems
Mining user similarity from semantic trajectories
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
An effective approach for mining mobile user habits
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Web user segmentation based on a mixture of factor analyzers
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Mining multilevel and location-aware service patterns in mobile web environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exploiting enriched contextual information for mobile app classification
Proceedings of the 21st ACM international conference on Information and knowledge management
On mining mobile apps usage behavior for predicting apps usage in smartphones
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Personalized point-of-interest recommendation by mining users' preference transition
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
Discovering similar users with respect to their habits plays an important role in a wide range of applications, such as collaborative filtering for recommendation, user segmentation for market analysis, etc. Recently, the progressing ability to sense user contexts of smart mobile devices makes it possible to discover mobile users with similar habits by mining their habits from their mobile devices. However, though some researchers have proposed effective methods for mining user habits such as behavior pattern mining, how to leverage the mined results for discovering similar users remains less explored. To this end, we propose a novel approach for conquering the sparseness of behavior pattern space and thus make it possible to discover similar mobile users with respect to their habits by leveraging behavior pattern mining. To be specific, first, we normalize the raw context log of each user by transforming the location-based context data and user interaction records to more general representations. Second, we take advantage of a constraint-based Bayesian Matrix Factorization model for extracting the latent common habits among behavior patterns and then transforming behavior pattern vectors to the vectors of mined common habits which are in a much more dense space. The experiments conducted on real data sets show that our approach outperforms three baselines in terms of the effectiveness of discovering similar mobile users with respect to their habits.