Automatically Learning User Preferences for Personalized Service Composition
Yu Zhao, Shaohua Wang, Ying Zou, Joanna Ng, Tinny Ng · 2017
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.