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Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and ARIMA model
Stockholm University, Faculty of Social Sciences, Stockholm Business School. University of Chinese Academy of Sciences, China.
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Number of Authors: 5
2018 (English)In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 107, p. 103-115Article in journal (Refereed) Published
Abstract [en]

The quality of service (QoS) of cloud services change frequently over time. Existing service recommendation approaches either ignore this property or address it inadequately, leading to ineffective service recommendation. In this paper, we propose a time-aware service recommendation (taSR) approach to address this issue. We first develop a novel similarity-enhanced collaborative filtering (CF) approach to capture the time feature of user similarity and address the data sparsity in the existing PITs (point in time). We then apply autoregressive integrated moving average model (ARIMA) to predict the QoS values in the future PIT under QoS instantaneity. We evaluate the proposed approach and compare it to the state-of-the-art. Our experimental results show that taSR achieves significant performance improvements over existing approaches.

Place, publisher, year, edition, pages
2018. Vol. 107, p. 103-115
Keyword [en]
Cloud service, Time-aware recommendation, QoS, Similarity-enhanced CF, ARIMA
National Category
Computer and Information Sciences Economics and Business
Identifiers
URN: urn:nbn:se:su:diva-155989DOI: 10.1016/j.dss.2017.12.012ISI: 000428000300009OAI: oai:DiVA.org:su-155989DiVA, id: diva2:1205306
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-05-14Bibliographically approved

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