SERVICE RECOMMENDATION BASED ON SEPARATED TIME-AWARE COLLABORATIVE POISSON FACTORIZATION
With the booming of web service ecosystems, nding suitable services and making service compositions have become an principal challenge for inexperienced developers. Therefore, recommending services based on service composition queries turns out to be a promising solution. Many recent studies apply Latent Dirichlet Allocation (LDA) to model the queries and services' description. However, limited by the restrictive assumption of the Dirichlet-Multinomial distribution assumption, LDA cannot generate highquality latent presentation, thus the accuracy of recommendation isn't quite satisfactory. Based on our previous work, we propose a Separated Time-aware Collaborative Poisson Factorization (STCPF) to tackle the problem in this paper. STCPF takes Poisson Factorization as the foundation to model mashup queries and service descriptions separately, and incorporates them with the historical usage data together by using collective matrix factorization. Experiments on the real-world show that our model outperforms than the state-of-the-art methods (e.g., Time-aware collaborative domain regression) in terms of mean average precision, and costs much less time on the sparse but massive data from web service ecosystem.
Liu, Xumin, and I. Fulia (2015), Incorporating User, Topic, and Service Related Latent Factors
into Web Service Recommendation, IEEE International Conference on Web Services, pp. 185-192.
J. Zhang, W. Tan, J. Alexander, I. Foster and R. Madduri (2011), Recommend-As-You-Go: A
Novel Approach Supporting Services-Oriented Scienti c Work
ow Reuse, IEEE International Con-
ference on Services Computing, pp. 48-55.
R Kalpana, K. Saruladha and J. Jayabharathy (2016), Studying the performance of QoS speci c
web service recommendation system using virtual regions, J. Web Engineering, Vol.16, pp. 361-396.
Q. Yu (2012), Decision tree learning from incomplete qos to bootstrap service recommendation,
International Conference on Web Services IEEE, pp. 194-201.
Z. Zheng, H. MaM. R. Lyu and I. King (2011), Qos-aware web service recommendation by collab-
orative ltering, IEEE Transactions on Services Computing, pp. 140-152.
U. Chukmol, A. Benharkat and Y. Amghar (2011), Bringing socialized semantics into web ser-
vices based on user-centric collaborative tagging and usage experience, IEEE Asia-Paci c Services
Computing Conference, Jeju, Korea DBLP, pp. 450-455.
F. Liu, L. Wang and J. Yu (2012), Context-aware similarity search of web service, IEEE Interna-
tional Conference on Information Science and Technology, pp. 596-602.
C. Platzer and S. Dustdar (2005), A Vector Space Search Engine forWeb Services, European
Conference on Web Services IEEE Computer Society, pp. 62.
T. Qiu, L. Li and P. Lin (2007), Web Service Discovery with UDDI Based on Semantic Similarity of
Service Properties, International Conference on Semantics, Knowledge and Grid IEEE Computer
Society, pp. 454-457.
K. C. Bhardwaj and R. K. Sharma (2015), Machine learning in ecient and e ective web service
discovery, J. Web Engineering Vol.14, pp. 196-214.
D. M. Blei, A. Y. Ng and M. I. Jordan (2003), Latent dirichlet allocation, Journal of Machine
Learning Research, Vol.3, pp. 993-1022.
Canny and John (2004), GaP: a factor model for discrete data, International ACM SIGIR Con-
ference on Research and Development in Information Retrieval ACM, pp. 122-129.
S. Chen, Y. Fan, W. Tan,J. Zhang, B Bai and Z. Gao (2016), Time-Aware Collaborative Poisson
Factorization for Service Recommendation, IEEE International Conference onWeb Services IEEE,
Y. Jiang, J. Liu, M. Tang and X. Liu (2011), An e ective web service recommendation method
based on personalized collaborative ltering, IEEE International Conference on Web Services IEEE
Computer Society, pp. 211-218.
P. Gopalan, L. Charlin and D. M. Blei (2014), Content-based recommendations with Poisson
factorization, Advances in Neural Information Processing Systems, Vol.4, pp. 3176-3184.
D. Liang, J. Paisley and DPW Ellis (2014), Codebook-based Scalable Music Tagging with Poisson
Matrix Factorization, ISMIR, Vol.4, pp. 167-172.
W. Zhang and J Wang (2015), A collective bayesian poisson factorization model for cold-start
local event recommendation, Proceedings of the 21th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, pp. 1455-1464.
Y. Zhong, Y. Fan, K. Huang and W. Tan (2015), Time-aware service recommendation for mashup
creation, IEEE Transactions on Services Computing, Vol.8, pp. 356-368.
B. Bing, Y. Fan, K. Huang and W. Tan, B. Xia and S. Chen (2015), Service Recommendation
for Mashup Creation Based on Time-Aware Collaborative Domain Regression, IEEE International
Conference on Web Services, pp. 209-216.
P. Gopalan, J. M. Hofman and D. M. Blei (2014), Scalable recommendation with poisson factor-
ization, Computer Science.
Y. Koren, R. Bell and C. Volinsky (2009), Matrix Factorization Techniques for Recommender
Systems, Computer, Vol.42, pp. 30-37.
W. Xu, X. Liu and Y. Gong (2003), Document clustering based on non-negative matrix factor-
ization, In Proceedings of the 26th annual international ACM SIGIR conference on Research and
development in informaion retrieval, pp. 30-37.
A. P. Singh and G. J. Gordon (2008), Matrix Factorization Techniques for Recommender Systems,
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data
mining, pp. 650-658.
MD Ho man, DM Blei, C Wang and J Paisley (2013), Stochastic variational inference, Journal of
Machine Learning Research, Vol.14, pp. 1303-1347.
J. Mcdowall and L. Kerschberg (2012), SLeveraging Social Networks to Improve Service Selection
ow Composition, Proceedings of the 2012 International Conference on Advances in Social
Networks Analysis and Mining, pp. 1278-1283.
W. Xu, J. Cao, L. Hu and J. Wang (2013), A social-aware service recommendation approach for
mashup creation, IEEE 20th International Conference on Web Services, pp. 1107-114.
S. Kamath and AV S. (2016), Semantic similarity based context-aware web service discovery using nlp techniques, J. Web Engineering, Vol.15, pp. 110-139.
K. Huang, J. Han, S. Chen and Z. Feng (2016). A Skewness-Based Framework for Mobile App
Permission Recommendation and Risk Evaluation, Service-Oriented Computing. Springer Inter-
B. Jiang, XX Zhang, WF Pan and B. Hu (2013), BIGSIR: A Bipartite Graph Based Service
Recommendation Method, IEEE Ninth World Congress on Services, pp. 363-369.
D. D. Lee and H. S. Seung (1999), Learning the parts of objects by non-negative matrix factoriza-
tion, Nature, pp. 788-791.
A. Schein, J. Paisley, D. M. Blei and H. Wallach (2015), Bayesian poisson tensor factorization
for inferring multilateral relations from sparse dyadic event counts, Proceedings of the 21th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1045-1054.
C. Hu, P Rai, C. Chen, M. Harding and L. Carin (2015), Scalable bayesian non-negative ten-
sor factorization for massive count data, Joint European Conference on Machine Learning and
Knowledge Discovery in Databases, pp. 53-70.