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RECSYS
2010
ACM
13 years 7 months ago
Collaborative filtering via euclidean embedding
Recommendation systems suggest items based on user preferences. Collaborative filtering is a popular approach in which recommending is based on the rating history of the system. O...
Mohammad Khoshneshin, W. Nick Street
KDD
2006
ACM
170views Data Mining» more  KDD 2006»
14 years 8 months ago
Classification features for attack detection in collaborative recommender systems
Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
SIGIR
2012
ACM
11 years 10 months ago
TFMAP: optimizing MAP for top-n context-aware recommendation
In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filterin...
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, ...
IDEAS
2006
IEEE
109views Database» more  IDEAS 2006»
14 years 1 months ago
Collaborative Filtering Process in a Whole New Light
Collaborative Filtering (CF) Systems are gaining widespread acceptance in recommender systems and ecommerce applications. These systems combine information retrieval and data mini...
Panagiotis Symeonidis, Alexandros Nanopoulos, Apos...
WEBI
2010
Springer
13 years 5 months ago
Reducing the Cold-Start Problem in Content Recommendation through Opinion Classification
Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The perform...
Damien Poirier, Françoise Fessant, Isabelle...