Name
Title | ||
|---|---|---|
Temporal Influences-Aware Collaborative Filtering for QoS-Based Service Recommendation |
Abstract | ||
|---|---|---|
As service computing becomes increasingly prevalent, the number of web services grows rapidly. It becomes very important to recommend suitable, personalized web services to users. Collaborative Filtering based on Quality of Service (QoS) has been widely used for service recommendation, and variety of factors such as location, environment are taken into account to improve the accuracy of recommendation. However, temporal influences, which is one of key factors affecting the QoS, are not fully considered by the investigators. In this paper, we propose a novel temporal influences-aware collaborative filtering method which designs an enhanced temporal influences-aware similarity measurement to predict QoS values. Finally, we conduct a series of experiments to evaluate the effectiveness of our method, and results show that our method outperforms other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/SCC.2017.67 | 2017 IEEE International Conference on Services Computing (SCC) |
Keywords | Field | DocType |
service recommendation,temporal influencesaware,QoS,collaborative filtering | Services computing,Mobile QoS,World Wide Web,Collaborative filtering,Computer science,Quality of service,Filter (signal processing),Prediction algorithms,Web service,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-5386-2006-9 | 1 | 0.35 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinglin Li | 1 | 150 | 30.39 |
| Jie Wang | 2 | 48 | 7.74 |
| Qibo Sun | 3 | 6 | 5.88 |
| Ao Zhou | 4 | 187 | 28.14 |