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» Predicting Neighbor Goodness in Collaborative Filtering
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RECSYS
2009
ACM
14 years 2 days ago
Context-based splitting of item ratings in collaborative filtering
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users’ ratings for items. It assumes that the users’ previously recorded ratin...
Linas Baltrunas, Francesco Ricci
ICDM
2005
IEEE
168views Data Mining» more  ICDM 2005»
13 years 11 months ago
A Scalable Collaborative Filtering Framework Based on Co-Clustering
Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular ...
Thomas George, Srujana Merugu
AH
2008
Springer
13 years 12 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
IAT
2009
IEEE
14 years 9 days ago
Social Trust-Aware Recommendation System: A T-Index Approach
Collaborative Filtering based on similarity suffers from a variety of problems such as sparsity and scalability. In this paper, we propose an ontological model of trust between us...
Alireza Zarghami, Soude Fazeli, Nima Dokoohaki, Mi...
CORR
2007
Springer
95views Education» more  CORR 2007»
13 years 5 months ago
Slope One Predictors for Online Rating-Based Collaborative Filtering
Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with pred...
Daniel Lemire, Anna Maclachlan