An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Content-Independent Task-Focused Recommendation
IEEE Internet Computing
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
SERF: integrating human recommendations with search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 08
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
An intelligent fuzzy-based recommendation system for consumer electronic products
Expert Systems with Applications: An International Journal
VCR: Virtual community recommender using the technology acceptance model and the user's needs type
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Developing an ontology-supported information integration and recommendation system for scholars
Expert Systems with Applications: An International Journal
Context-aware recommendation using rough set model and collaborative filtering
Artificial Intelligence Review
Personalized context-aware QoS prediction for web services based on collaborative filtering
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
MUCS: A model for ubiquitous commerce support
Electronic Commerce Research and Applications
Exploring airport traffic capability using Petri net based model
Expert Systems with Applications: An International Journal
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Is the contextual information relevant in text clustering by compression?
Expert Systems with Applications: An International Journal
A social recommender mechanism for improving knowledge sharing in online forums
Information Processing and Management: an International Journal
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Hi-index | 12.06 |
It has been recognized that recommendation system is a very important and indispensable topic in E-commerce. Many famous E-commerce websites utilize recommendation systems to convert browsers into buyers. The forms of recommendation include suggesting products/services to the customer, providing personalized product/service information, summarizing community opinion, and providing community critiques. Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches, however, have their own drawbacks. This study proposes the integrated contextual information as the foundation concept of multidimensional recommendation model, and uses the online analytical processing (OLAP) ability of data warehousing to solve the contradicting problems among hierarchy ratings. The evaluation studies show that by establishing additional customer profiles and using multidimensional analyses to find the key factors affecting customer perceptions, the proposed approach increases the recommendation quality.