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STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 9 months ago
Probabilistic Association Rules for Item-Based Recommender Systems
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
Sylvain Castagnos, Armelle Brun, Anne Boyer
EPIA
2009
Springer
13 years 11 months ago
Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations
Abstract. In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it is typi...
Catarina Miranda, Alípio Mário Jorge
SIBGRAPI
2007
IEEE
14 years 1 months ago
Multiple Mice Tracking using a Combination of Particle Filter and K-Means
This paper presents a new approach to multiple objects tracking that combines particle filters and k-means. The approach has been tested under an important real world situation, ...
Wesley Nunes Gonçalves, João Bosco O...
WWW
2007
ACM
14 years 8 months ago
Google news personalization: scalable online collaborative filtering
Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several million users and items) and dynamic (the underlying item ...
Abhinandan Das, Mayur Datar, Ashutosh Garg, ShyamS...
WETICE
2005
IEEE
14 years 1 months ago
CAFE - Collaborative Agents for Filtering E-mails
CAFE (Collaborative Agents for Filtering E-mails) is a multi-agent system to collaboratively filter spam from users’ mail stream. CAFE associates a proxy agent with each user, a...
Lorenzo Lazzari, Marco Mari, Agostino Poggi