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A Web service QoS prediction approach based on time- and location-aware collaborative filtering

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Abstract

In QoS-based Web service recommendation, predicting quality of service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. CF algorithms can be divided into two main categories: memory-based and model-based algorithms. Memory-based CF algorithms are easy to implement and highly effective, but they suffer from a fundamental problem: inability to scale-up. Model-based CF algorithms, such as clustering CF algorithms, address the scalability problem by seeking users for recommendation within smaller and highly similar clusters, rather than within the entire database. However, they are often time-consuming to build and update. In this paper, we propose a time-aware and location-aware CF algorithms. To validate our algorithm, this paper conducts series of large-scale experiments based on a real-world Web service QoS data set. Experimental results show that our approach is capable of addressing the three important challenges of recommender systems–high quality of prediction, high scalability, and easy to build and update.

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Notes

  1. http://www.wsdream.net/wsdream/dataset.html.

  2. An autonomous system (AS) is either a single network or a group of networks that is controlled by a common network administrator on behalf of a single administrative entity (such as a university and a business enterprise). An autonomous system has a globally unique number.

  3. ASN denotes globally unique number of the AS to which the IP belongs. Note that even if users are within a same AS, it does not definitely mean that they are close geographically, and vice versa. Generally speaking, intra-AS traffic is much better than inter-AS traffic regarding transmission performance such as response time. Therefore, even if two users are located in the same city, they may seem far away from each other in terms of network distance if their computers are within different ASs.

  4. http://hc.apache.org/.

  5. http://www.team-cymru.org/Services/ip-to-asn.html#dns.

  6. http://www.wsdream.net/wsdream/dataset.html.

  7. http://radlab.sjtu.edu.cn/?p=154.

  8. http://whois.cymru.com/.

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Acknowledgments

The work described in this paper was supported by the National Natural Science Foundation of China under Grant Nos. 91118004, 61232007 and the Innovation Program of Shanghai Municipal Education Commission (No. 13ZZ023).

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  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Chengyuan Yu & Linpeng Huang

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  1. Chengyuan Yu
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  2. Linpeng Huang
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Correspondence to Chengyuan Yu.

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Yu, C., Huang, L. A Web service QoS prediction approach based on time- and location-aware collaborative filtering. SOCA 10, 135–149 (2016). https://doi.org/10.1007/s11761-014-0168-4

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