Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommender systems for evaluating computer messages
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Revised Papers from the HUMACS, DASWIS, ECOMO, and DAMA on ER 2001 Workshops
A music recommendation system based on music and user grouping
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
| Hi-index | 12.05 |
Collaborative filtering is an extensively adopted approach for commodity recommendation. This investigation presents a collaborative filtering method to support commodity recommendation of retail business according to customer preferences. Moreover, a novel recommendation methodology based on decision tree induction is also proposed to obtain further effectiveness and quality of recommendations. Effectiveness of the proposed method is evaluated by implementing a recommender system based on data mining and analyzing real retail business data to demonstrate the operability of the system.