Title :
A fast collaborative filtering algorithm for implicit binary data
Author :
Bu, Manzhao ; Luo, Shijian ; He, Ji
Author_Institution :
Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Abstract :
Item-based and user-based collaborative filtering are two well-known algorithms for recommender system in e-commerce. Both the algorithms make use of similarity matrix whose elements represent the similarity of each item pairs or user pairs. A fast algorithm for item-based similarity matrix computation using cosine similarity metric was reviewed and applied for user-based one with some modification. The results show that the fast algorithm can blend well with other similarity metrics, and it can greatly improve the computational performance.
Keywords :
electronic commerce; information filtering; matrix algebra; cosine similarity metric; e-commerce; implicit binary data; item-based collaborative filtering; item-based similarity matrix computation; recommender system; user-based collaborative filtering; Collaboration; Computer science; Educational institutions; Electronic commerce; Filtering algorithms; Helium; Information filtering; Information filters; Recommender systems; Scalability; Binary data; Collaborative filtering; Recommender system; Similarity matrix;
Conference_Titel :
Computer-Aided Industrial Design & Conceptual Design, 2009. CAID & CD 2009. IEEE 10th International Conference on
Conference_Location :
Wenzhou
Print_ISBN :
978-1-4244-5266-8
Electronic_ISBN :
978-1-4244-5268-2
DOI :
10.1109/CAIDCD.2009.5374935
