WS-HFS: A Heterogeneous Feature Selection Framework for Web Services Mining

With the development of Service Computing and Big Data research, more and more heterogeneous data generated in the process of Service Computing attracts our attention. Combining correlated data sources may help improve the performance of a given task. For example, in service recommendation, one can combine (1) user profile data (e.g. Genders, age, etc.), (2) user log data (e.g., Click through data, service invocation records, etc.), (3) QoS data (e.g. Response time, cost, etc.), (4) service functional description (e.g., Service name, WSDL document, etc.) and (5) service tagging data (i.e., Tags annotated by users) to build a recommendation model. All these data sources provide informative but heterogeneous features. For instance, user profile and QoS data usually have nominal features reflecting users' background and services' qualities, log data provides term-based features about users' historical behaviors, and service functional description and tagging data have term-based features reflecting services' functionalities and users' collective opinions. Given multiple heterogeneous data sources, one important challenge is to find a unified feature subspace to capture the knowledge from all data sources. To handle this problem, in this paper, we propose a Heterogeneous Feature Selection framework, named as WS-HFS, in which the consensus and the weight of different sources are both considered. Moreover, we apply the proposed framework to Web service clustering as a case study, and compare it with the state of the art approaches. The comprehensive experiments based on real data demonstrate the effectiveness of WS-HFS.