SERVICE RECOMMENDATION BASED ON SEPARATED TIME-AWARE COLLABORATIVE POISSON FACTORIZATION

  • SHUHUI CHEN Department of Automation, Tsinghua University Beijing, China
  • YUSHUN FAN Department of Automation, Tsinghua University Beijing, China
  • WEI TAN IBM Thomas J. WatsonResearch Center Yorktown Heights, New York
  • JIA ZHANG Department of Electrical and Computer Engineering, Carnegie Mellon University Silicon Valley, California
  • BING BAI Department of Automation, Tsinghua University Beijing, China
  • ZHENFENG GAO Department of Automation, Tsinghua University Beijing, China
Keywords: service recommendation, service composition, Time-aware, Poisson Factorization

Abstract

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.

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Published
2017-05-11
Section
Articles