Automated repair of scoring rules in constraint-based recommender systems

2013 ◽  
Vol 26 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Alexander Felfernig ◽  
Stefan Schippel ◽  
Gerhard Leitner ◽  
Florian Reinfrank ◽  
Klaus Isak ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Scoring Rules

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

Context-Aware Recommender Systems

2012 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Li-Cai WANG ◽  
Xiang-Wu MENG ◽  
Yu-Jie ZHANG
Keyword(s):  
Recommender Systems ◽  
Context Aware

An Unsupervised Algorithm for Detecting Shilling Attacks on Recommender Systems

2011 ◽  
Vol 37 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Cong LI ◽  
Zhi-Gang LUO ◽  
Jin-Long SHI
Keyword(s):  
Recommender Systems
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