PROJECT TITLE :
NCSR: Negative-Connection-Aware Service Recommendation for Large Sparse Service Network
Currently, most net service recommendation studies focus on mining association patterns among services from historical compositions and recommending correct services primarily based on patterns derived. However, latent negative patterns that indicate the inappropriate mixtures of services, are largely ignored. Therefore, by combining additional negative patterns with the already-exploited positive patterns in the big spares network of web services, we present a additional comprehensive and correct model for service recommendation. A lot of specifically, we mix positive and negative composition patterns mined from service annotated tags. The extensive experiments conducted on a real-life dataset show that our methodology can outperform not solely ancient APriori -primarily based recommendation method however conjointly Link Prediction-based one. The experiments on a artificial dataset show that our methodology will additionally be effective to make recommendations in giant-scale service network.
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