Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Agent-oriented technology in support of e-business
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
RecTree: An Efficient Collaborative Filtering Method
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Kansei Retrieval Method Based on Design Pattern of Traditional Japanese Crafting Object
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
IEEE Transactions on Knowledge and Data Engineering
Multimedia-based interactive advising technology for online consumer decision support
Communications of the ACM - Special issue: RFID
Expert Systems with Applications: An International Journal
Guest Editors' Introduction: Recommender Systems
IEEE Intelligent Systems
A review of associative classification mining
The Knowledge Engineering Review
A Lazy Approach to Associative Classification
IEEE Transactions on Knowledge and Data Engineering
Knowledge-based interactive selling of financial services with FSAdvisor
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
A discussion of two major benefits of using agents in software evelopment
ESAW'02 Proceedings of the 3rd international conference on Engineering societies in the agents world III
Data mining for web personalization
The adaptive web
Collaborative filtering recommender systems
The adaptive web
Hybrid web recommender systems
The adaptive web
An experimental study for the selection of modules and facilities in a mass customization context
Journal of Intelligent Manufacturing
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Planning Product Configurations Based on Sales Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Customized products recommendation based on probabilistic relevance model
Journal of Intelligent Manufacturing
| Hi-index | 0.00 |
Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool's effectiveness, usefulness and reliability among other parameters.