Neural Networks
Self-organizing maps
Soft computing in case based reasoning
Soft computing in case based reasoning
Soft computing in case based reasoning
A genetic algorithm and growing cell structure approach to learning case retrievel structures
Soft computing in case based reasoning
Adaptation of cases for case based forecasting with neural network support
Soft computing in case based reasoning
Maintaining knowledge about temporal intervals
Communications of the ACM
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
WWW Assisted Browsing by Reusing Past Navigations of a Group of Users
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Representing Temporal Knowledge for Case-Based Prediction
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
COBRA: A CBR-Based Approach for Predicting Users Actions in a Web Site
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Operator Decision Aiding by Adaption of Supervision Strategies
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Automating Personal Categorization Using Artificial Neural Networks
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Applied Intelligence
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Phoneme recognition using time-dependent versions of self-organizing maps
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Mining web logs: applications and challenges
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CBR for Advice Giving in a Data-Intensive Environment
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Integrating knowledge-based system and neural network techniques for robotic skill acquisition
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Predicting student help-request behavior in an intelligent tutor for reading
UM'03 Proceedings of the 9th international conference on User modeling
Case base maintenance for improving prediction quality
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Classification and tracking of hypermedia navigation patterns
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Fuzzy modeling of digital products pricing in the virtual marketplace
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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In this paper we present Casep2: a hybrid neuro-symbolic system combining case-based reasoning (CBR) and artificial neural networks that aims at clustering and classifying users' behavior in an e-commerce site. A user behavior is represented by a sequence of visited web pages, in a session. Each registered behavior is associated to one of the following classes: buyer or non-buyer. Our goal is to provide a system that mines the web site access log in order to predict the class of an on-going user navigation. One major challenge to face is to provide scalable algorithms that can handle efficiently the large amount of data to learn from. Predictions should be made in real-time, during the current navigation. In addition, raw data has a sequential nature and are very noisy. In the proposed system, two original neural networks, named M-SOM-ART networks, are applied: one to implement the retrieval phase of a CBR cycle, and the second to implement the reuse phase. This hybrid scheme allows to ensure incremental learning as well as efficient treatment of large-scale sequential data. Experiments on real log data of an e-commerce site show the relevancy of the proposed approach.