Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Communications of the ACM
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Machine Learning
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Sequential Patterns under a Similarity Constraint
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
ADMIT: anomaly-based data mining for intrusions
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Binary Prediction Based on Weighted Sequential Mining Method
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Similarity Measurement of Web Sessions by Sequence Alignment
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
A General Model for Sequential Pattern Mining with a Progressive Database
IEEE Transactions on Knowledge and Data Engineering
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
S2MP: similarity measure for sequential patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
| Hi-index | 0.00 |
Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capability to recommend the best next-items to each specific target user. In this paper, we introduce a personalized sequential pattern mining-based recommendation framework. Using a novel Competence Score measure, the proposed framework effectively learns user-specific sequence importance knowledge, and exploits this additional knowledge for accurate personalized recommendation. Experimental results on real-world datasets demonstrate that the proposed framework effectively improves the efficiency for mining sequential patterns, increases the user-relevance of the identified frequent patterns, and most importantly, generates significantly more accurate next-items recommendation for the target users.