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» Evaluation of Item-Based Top-N Recommendation Algorithms
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SIGIR
2010
ACM
13 years 9 months ago
Temporal diversity in recommender systems
Collaborative Filtering (CF) algorithms, used to build webbased recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current eva...
Neal Lathia, Stephen Hailes, Licia Capra, Xavier A...
SIGIR
2012
ACM
11 years 7 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, ...
GFKL
2005
Springer
114views Data Mining» more  GFKL 2005»
13 years 10 months ago
Attribute-aware Collaborative Filtering
One of the key challenges in large information systems such as online shops and digital libraries is to discover the relevant knowledge from the enormous volume of information. Rec...
Karen H. L. Tso, Lars Schmidt-Thieme
CIKM
2005
Springer
13 years 10 months ago
Feature-based recommendation system
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems—a personalized information filtering technology use...
Eui-Hong Han, George Karypis
WWW
2009
ACM
14 years 1 days ago
Tagommenders: connecting users to items through tags
Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navig...
Shilad Sen, Jesse Vig, John Riedl