NDLI: OS_Searching: Optimized Skeleton based Recommender Searching Mechanism for the Trust-Aware Recommender System

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OS_Searching: Optimized Skeleton based Recommender Searching Mechanism for the Trust-Aware Recommender System

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OS_Searching: Optimized Skeleton based Recommender Searching Mechanism for the Trust-Aware Recommender System

Content Provider	ACM Digital Library
Author	Lee, Young-Koo Guan, Donghai Yuan, Weiwei Kim, Taeyeon
Abstract	Sufficient number of recommenders should be efficiently found for active users of the trust-aware recommender system (TARS). S_Searching is a newly proposed recommender searching mechanism for TARS based on the scale-freeness of the trust network. It first propagates the user trust from the active user to the skeleton, and then finds the recommenders via trust propagation from the skeleton, in which the skeleton is the collection of nodes with the highest degrees in the trust network. However, S_Searching does not consider the skeleton maintenance cost or the coverage overlap between the nodes of the skeleton. This paper proposes a recommender searching mechanism called OS_Searching to improve S_Searching by optimizing the skeleton selection strategy. OS_Searching uses the genetic algorithm to choose the most optimized skeleton. It can achieve the maximum prediction coverage with the minimum skeleton maintenance cost. Simulation results show that compared with S_Searching, OS_Searching can reduce the skeleton maintenance cost by more than 90% with reasonable prediction coverage.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450341424
DOI	10.1145/2857546.2857646
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-01-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Recommender searching Skeleton selection Trust-aware recommender system Genetic algorithm
Content Type	Text
Resource Type	Article

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