Automatically Learning User Preferences for Personalized Service Composition

Yu Zhao, Shaohua Wang, Ying Zou, Joanna Ng, Tinny Ng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

With the rapid growth of the web services technologies, users often leverage various web services to perform their daily activities, such as on-line shopping. Due to the massive amount of web services available, a user faces numerous choices to meet their personal preferences when selecting the desired services from the web services with the similar functionality. Therefore, it becomes tedious and cumbersome tasks for users to discover and compose services. To reduce user's cognitive burden, it is critical to support automated service composition and make efficient recommendation of personalized services to achieve user's overall goals. However, existing approaches only offer users with limited options designed for the interest of a group of users without considering individual users' interests. To allow users to compose personalized services without much manual specification, we propose a machine learning approach that applies a learning-to-rank algorithm, RankBoost, to automatically learn user preferences and the prioritization of the preferences from users' historical data. Moreover, our approach uses the multi-objective reinforcement learning (MORL) algorithm to make trade-offs among user preferences and recommends a collection of services to achieve the highest objective. We conduct an empirical study to evaluate our approach by collecting the historical data from 12 subjects. The results demonstrate that our approach outperforms the two well-established baseline approaches by 100%-200% in terms of precision on recommending services.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017
EditorsShiping Chen, Ilkay Altintas
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages776-783
Number of pages8
ISBN (Electronic)9781538607527
DOIs
StatePublished - Sep 7 2017
Externally publishedYes
Event24th IEEE International Conference on Web Services, ICWS 2017 - Honolulu, United States
Duration: Jun 25 2017Jun 30 2017

Publication series

NameProceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017

Other

Other24th IEEE International Conference on Web Services, ICWS 2017
CountryUnited States
CityHonolulu
Period6/25/176/30/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems and Management

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