Your neighbors alleviate cold-start: On geographical neighborhood influence to collaborative web service QoS prediction

Web service quality of service prediction via regional reputation‐based matrix factorization

“…This method has used the geographic information of services and users for clustering and then combined this information with MF to gain more accuracy in prediction 40 . Colbar, LMF‐PP, and REMF are other prediction methods that have used the location information for more accurate predictions 13,16,41,42 . Therefore, using the location information of the users can improve the accuracy of the predictions, because the users located in the same geographical location have similar QoS values.…”

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

“…Wu et al proposed a context-aware MF framework by integrating context-aware neighborhood [37]. Our group also proposed a bottom-up neighbor discovery algorithm based on contextaware tree structure [38], and MF is expanded by integrating the context aware neighborhood. Yang et al fed the selected neighbor directly into FM model to make prediction [25], demonstrating that integrating context information can indeed enhance the performance of CF-, MF-and FM-based methods.…”

A Two-Tier Service Filtering Model for Web Service QoS Prediction

“…Jiang et al [17] considered social text information in the recommendation process. Chen et al [18] proposed an MF model based on geographical neighborhood knowledge, which can alleviate the cold start problem. To better represent QoS data, Guo et al [19] proposed three product recommendation models based on implicit user feedback combined with social trust, and introduced a matrix factorization technique to restore user preference between rated items and unrated items according to user-user and item-item similarity.…”