Abstract
QoS-based Web service recommendation is a widely employed way of selecting proper candidate services that can provide better QoS to users. In this paper, based on Matrix Factorization (MF) model, at first, we propose the service neighborhood extended MF model (SN-EMF) and user neighborhood-extended MF model (UN-EMF), in which we integrate two types of valuable information respectively. One is the historical QoS records of the k most similar neighbors, and the other is the geographical location information. Then, with aggregating the results together, we unify the two models into a unified service recommending framework (U-EMF), which can be divided into two parts of offline and online. Moreover, we also discuss some strategies about model selection and simplification. In the end, we conduct sufficient experiments, showing the effectiveness of our three models, especially the relatively large performance improvement of U-EMF model.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call