Journal of Applied Mathematics · 2013 · 99 citations · 13 references
ComputingComputer ScienceInformation TechnologyData MiningInformation RetrievalRecommender SystemsCollaborative FilteringEvaluation Of Recommender SystemsRecommendation EvaluationRecommendation SystemsSocial Network AnalysisSocial RecommendationPrecision-recall TradeoffSimilarity ComputationTag Recommendation
Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.
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Linyuan Lü, Matúš Medo, Chi Ho Yeung et al. · Physics Reports · 2012 · 983 citations · Full text
SLIM: Sparse Linear Methods for Top-N Recommender Systems
Xia Ning, George Karypis · 2011 · 738 citations
Alan Mislove, Bimal Viswanath, Krishna P. Gummadi et al. · 2010 · 670 citations