7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
https://doi.org/10.1016/j.asoc.2019.105762
Copy DOIJournal: Applied Soft Computing | Publication Date: Sep 16, 2019 |
Citations: 24 |
With the advent of the era “everything is service ”, the emergence of Web services on the Internet is experiencing an exponential growth trend. How to recommend services to users that utilize sequential historical records has become one of the most challenging research topics in service computing. Tensor Factorization (TF) and Long Short Term Memory (LSTM) networks are two typical application paradigms for sequential service recommendation tasks. However, TF can only learn static short-term dependency patterns between users and services, ignoring the dynamic long-term dependency patterns between users and services. Although LSTM in Deep Leaning can learn dynamic long-term dependency patterns, it often encounters the trouble of vanishing gradient due to its complex gated mechanism. To address these critical challenges, we develop a novel model based on Deep Learning named Recurrent Tensor Factorization (RTF) with three innovations: (1) Three-dimensional interaction tensor of user–service-time was granulated into three fixed-size embedding dense vectors. (2) Personalized Gated Recurrent Unit (PGRU) and Generalized Tensor Factorization (GTF) simultaneously work on shared embedding dense vectors to memorize the long-term and short-term dependency patterns between users and services respectively. (3) Armed with GTF and PGRU, RTF is competent to predict the unknown Quality of Service (QoS) through comprehensive analysis. Experiments are conducted on real-world dataset, and the results indicate that our proposed method obviously outperforms six state-of-the-art time-aware service recommendation methods.
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.