Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

By Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King. IEEE Transactions on Services Computing, 2013

439 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Temporal QoS-aware web service recommendation via non-negative tensor factorization (2014, methodology): “Recently, some researchers [14, 17, 30] blend them for obtaining better performance.”
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): “Various approaches are proposed to address this issue, and latent factor analysis (LFA)-based ones [7], [8], [17]–[19] are becoming increasingly attractive.”
An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems (2014, background): “, user-side QoS prediction in service computing [21], [22], and other areas with the need of sparse-matrix-analysis subject to non-negativity.”
QoS-Aware Web Service Recommendation via Collaborative Filtering (2014, methodology): “[40] combine the user-based and item-based CF algorithms to recommend Web services; They also integrate Neighborhood approach with Matrix Factorization in work [39].”