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2016 IEEE International Conference on Web Services (ICWS) (2016)
San Francisco, CA, USA
June 27, 2016 to July 2, 2016
ISBN: 978-1-5090-2676-0
pp: 196-203
With the booming number of web services, it is a challenge for inexperienced developers to select suitable services and make service compositions. Therefore, recommending services based on user queries becomes a necessity. For modeling the queries and services' descriptions, many recent studies are based on LDA (Latent Dirichlet Allocation). However, some previous empirical works indicate that LDA model doesn't gain high accuracy in generating latent presentation which is subject to the restrictive assumption of the Dirichlet-Multinomial distribution. In this paper, we propose a Time-aware Collaborative Poisson Factorization (TCPF) to tackle the problem. TCPF takes Poisson Factorization as the foundation to model mashup queries and service descriptions separately, and incorporate them with the historical usage data together using collective matrix factorization. Experiments on the real-world ProgrammableWeb dataset show that our model outperforms the state-of-the-art methods (e.g., Time-aware collaborative domain regression) by 7.7% in terms of mean average precision, and costs much less time on the sparse, massive and long-tailed data set.
Conferences, Web services

S. Chen, Y. Fan, W. Tan, J. Zhang, B. Bai and Z. Gao, "Time-Aware Collaborative Poisson Factorization for Service Recommendation," 2016 IEEE International Conference on Web Services (ICWS), San Francisco, CA, USA, 2016, pp. 196-203.
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