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
Personalization on the Net using Web mining: introduction
Communications of the ACM
Data Mining and Personalization Technologies
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Adaptive decision support system (ADSS) for B2C e-commerce
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Electronic promotion to new customers using mkNN learning
Information Sciences: an International Journal
Spontaneous interaction with audiovisual contents for personalized e-commerce over Digital TV
Expert Systems with Applications: An International Journal
Receiver-side semantic reasoning for digital TV personalization in the absence of return channels
Multimedia Tools and Applications
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
An endorser discovering mechanism for social advertising
Proceedings of the 11th International Conference on Electronic Commerce
Automatic Generation of Mashups for Personalized Commerce in Digital TV by Semantic Reasoning
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Designing Tools for Supporting User Decision-Making in e-Commerce
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Government-to-business personalized e-services using semantic-enhanced recommender system
EGOVIS'11 Proceedings of the Second international conference on Electronic government and the information systems perspective
Electronic Commerce Research and Applications
Engineering Applications of Artificial Intelligence
Property-based collaborative filtering for health-aware recommender systems
Expert Systems with Applications: An International Journal
Taxonomy-Oriented recommendation towards recommendation with stage
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Product recommendation with temporal dynamics
Expert Systems with Applications: An International Journal
On-line dynamic adaptation of fuzzy preferences
Information Sciences: an International Journal
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
International Journal of Business Information Systems
International Journal of Business Information Systems
Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm
Information Systems Frontiers
| Hi-index | 12.06 |
Nowadays, Electronic Commerce (EC) provides a new gateway for customers shopping online. One of the most significant advantages offered by online shops is convenience. Online shopping is no longer a time-consuming task and, in fact, is an energy-saving activity. Therefore, shortening customers' product searching time is the key to an online shop's success. In order to serve customers instantly and efficiently, it is essential to recognize each customer's unique and particular needs and recommend a personalized shopping list. In this paper, we construct a recommendation system based on a modified product taxonomy and customer classification to identify customers' shopping behavior: product addictive, brand addictive or a hybrid addictive. By analyzing each customer's preferred brand or product, our proposed system can recommend products to customers either at the general or at the specific levels.