Time-aware cloud service recommendation using similarity-enhanced collaborative filtering and ARIMA model

• 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv (hsv//swe)

SOCIAL SCIENCES  -- Economics and Business (hsv//eng)

Keyword

Cloud service

Time-aware recommendation

QoS

Similarity-enhanced CF

ARIMA

Publication and Content Type

ref (subject category)

art (subject category)