Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 43, Issue 2, Pages 428-439Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCA.2012.2210409
Keywords
Neighborhood-based collaborative filtering (CF); quality-of-service (QoS) prediction; service selection
Funding
- National Technology Support Program [2011BAH15B05]
- National Natural Science Foundation of China [60873224]
- Zhejiang Provincial Natural Science Foundation of China [Y1110591]
- Science and Technology Program of Zhejiang Province [2008C03007]
- National Key Science and Technology Research Program of China [2009ZX01043-003-003]
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Quality-of-service-based (QoS) service selection is an important issue of service-oriented computing. A common premise of previous research is that the QoS values of services to target users are supposed to be all known. However, many of QoS values are unknown in reality. This paper presents a neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection. Compared with existing methods, the proposed method has three new features: 1) the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; 2) a data smoothing process to improve prediction accuracy; and 3) a similarity fusion approach to handle the data sparsity problem. In addition, a two-phase neighbor selection strategy is proposed to improve its scalability. An extensive performance study based on a public data set demonstrates its effectiveness.
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