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
The InfoFinder Agent: Learning User Interests through Heuristic Phrase Extraction
IEEE Expert: Intelligent Systems and Their Applications
Expert Systems with Applications: An International Journal
Using ontology network analysis for research document recommendation
Expert Systems with Applications: An International Journal
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Knowledge-based approach to improving micromarketing decisions in a data-challenged environment
Expert Systems with Applications: An International Journal
Electronic promotion to new customers using mkNN learning
Information Sciences: an International Journal
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
Using contextual information and multidimensional approach for recommendation
Expert Systems with Applications: An International Journal
Using data mining to provide recommendation service
WSEAS Transactions on Information Science and Applications
A multi-stage collaborative filtering approach for mobile recommendation
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
The personalized recommendation with bundling strategy based on product consuming period
CIS'09 Proceedings of the international conference on Computational and information science 2009
Salesman-like recommendation system based on visitor product knowledge and browsing actions
ISTASC'09 Proceedings of the 9th WSEAS International Conference on Systems Theory and Scientific Computation
Integrating web mining and neural network for personalized e-commerce automatic service
Expert Systems with Applications: An International Journal
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Assessing users' product-specific knowledge for personalization in electronic commerce
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Expert Systems with Applications: An International Journal
Price bundling for personalized recommendation
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
Autonomous rule induction from data with tolerances in customer relationship management
Expert Systems with Applications: An International Journal
Using genetic algorithms for personalized recommendation
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A multi-agent recommender system for supporting device adaptivity in e-Commerce
Journal of Intelligent Information Systems
The effects of location personalization on individuals' intention to use mobile services
Decision Support Systems
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm
International Journal of Business Information Systems
International Journal of Business Information Systems
International Journal of Business Information Systems
Hi-index | 12.07 |
Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a one-to-one marketing strategy can improve profitability for companies.