A fast collaborative filtering algorithm for implicit binary data
M. Bu, S. Luo, and J. He. Computer-Aided Industrial Design Conceptual Design, 2009. CAID CD 2009. IEEE 10th International Conference on, page 973 -976. (November 2009)
DOI: 10.1109/CAIDCD.2009.5374935
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.
Description
IEEE Xplore - A fast collaborative filtering algorithm for implicit binary data
%0 Conference Paper
%1 bu2009collaborative
%A Bu, Manzhao
%A Luo, Shijian
%A He, Ji
%B Computer-Aided Industrial Design Conceptual Design, 2009. CAID CD 2009. IEEE 10th International Conference on
%D 2009
%K algorithm binary cf collaborative filtering implementation
%P 973 -976
%R 10.1109/CAIDCD.2009.5374935
%T A fast collaborative filtering algorithm for implicit binary data
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5374935&tag=1
%X 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.
@inproceedings{bu2009collaborative,
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.},
added-at = {2012-08-30T10:13:03.000+0200},
author = {Bu, Manzhao and Luo, Shijian and He, Ji},
biburl = {https://www.bibsonomy.org/bibtex/2e34cd30370fd0785efa332ac52fae709/folke},
booktitle = {Computer-Aided Industrial Design Conceptual Design, 2009. CAID CD 2009. IEEE 10th International Conference on},
description = {IEEE Xplore - A fast collaborative filtering algorithm for implicit binary data},
doi = {10.1109/CAIDCD.2009.5374935},
interhash = {3dee7de10f6fb47256df1475d017313b},
intrahash = {e34cd30370fd0785efa332ac52fae709},
keywords = {algorithm binary cf collaborative filtering implementation},
month = {nov.},
pages = {973 -976},
timestamp = {2012-08-30T10:13:03.000+0200},
title = {A fast collaborative filtering algorithm for implicit binary data},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5374935&tag=1},
year = 2009
}