Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PHOAKS: a system for sharing recommendations
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Decision quality using ranked attribute weights
Management Science
A scalable comparison-shopping agent for the World-Wide Web
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Agent-Mediated Integrative Negotiation for Retail Electronic Commerce
AMET '98 Selected Papers from the First International Workshop on Agent Mediated Electronic Trading on Agent Mediated Electronic Commerce
Category cluster discovery from distributed WWW directories
Information Sciences—Informatics and Computer Science: An International Journal - special issue: Knowledge discovery from distributed information sources
Agent-mediated electronic commerce: a survey
The Knowledge Engineering Review
IEEE Transactions on Knowledge and Data Engineering
Feature-based recommendation system
Proceedings of the 14th ACM international conference on Information and knowledge management
Comparing methods for multiattribute decision making with ordinal weights
Computers and Operations Research
| Hi-index | 12.05 |
Recommendation methods, which suggest a set of products likely to be of interest to a customer, require a great deal of information about both the user and the products. Recommendation methods take different forms depending on the types of preferences required from the customer. In this paper, we propose a new recommendation method that attempts to suggest products by utilizing simple information, such as ordinal specification weights and specification values, from the customer. These considerations lead to an ordinal weight-based multi-attribute value model. This model is well suited to situations in which there exist insufficient data regarding the demographics and transactional information on the target customers, because it enables us to recommend personalized products with a minimal input of customer preferences. The proposed recommendation method is different from previously reported recommendation methods in that it explicitly takes into account multidimensional features of each product by employing an ordered weight-based multi-attribute value model. To evaluate the proposed method, we conduct comparative experiments with two other methods rooted in distance-based similarity measures.