QoS-Aware Web Service Recommendation via Collaborative Filtering

By X. Chen, Zibin Zheng, Michael R. Lyu. Web Services Foundations, 2014

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Investigating QoS of Real-World Web Services (2014, methodology): “A number of QoS prediction approaches have been proposed, including user-based QoS prediction approach [17], UIPCC [4], matrix factorization approach [5], ranking-oriented approach [18], RegionKNN [3], and so on.”
Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors (2020, background): “An LFA-based QoS-predictor takes a user–service QoS matrix as the fundamental data source [18]–[23], where each row denotes a speciﬁed user, each column denotes a speciﬁed service, and each element denotes the QoS record of a certain metric experienced by a speciﬁed user on a speciﬁed service.”
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization (2013, methodology): “[23] propose a neighborhood-based approach (i.”
QoS Ranking Prediction for Cloud Services (2013, methodology): “The most analyzed examples of memory-based collaborative filtering include user-based approaches [4], [9], item-based approaches [7], [11], [16], and their fusion [13], [16], [17], [22], [23].”