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CHI
2006
ACM
14 years 4 months ago
Accounting for taste: using profile similarity to improve recommender systems
Recommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers curr...
Philip Bonhard, Clare Harries, John D. McCarthy, M...
I3E
2008
234views Business» more  I3E 2008»
13 years 6 months ago
Development of Recommender Systems Using User Preference Tendencies: An Algorithm for Diversifying Recommendation
Abstract. Many e-commerce sites use a recommendation system to filter the specific information that a user wants out of an overload of information. Currently, the usefulness of the...
Yuki Ogawa, Hirohiko Suwa, Hitoshi Yamamoto, Isamu...
WWW
2009
ACM
14 years 5 months ago
Matchbox: large scale online bayesian recommendations
We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form o...
David H. Stern, Ralf Herbrich, Thore Graepel
AAAI
2006
13 years 5 months ago
Model-Based Collaborative Filtering as a Defense against Profile Injection Attacks
The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-...
Bamshad Mobasher, Robin D. Burke, Jeff J. Sandvig
AH
2008
Springer
13 years 11 months ago
Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations
Abstract. User-to-user similarity is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user similarity the ratings assigned by two users to a ...
Linas Baltrunas, Francesco Ricci