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An Approach for Context-Aware Service Discovery and Recommendation

Given the large amount of existing services and the diversified needs nowadays, it is time-consuming for end-users to find appropriate services. To help end-users obtain their desired services, context-aware systems provide a promising way to automatically search and recommend services using a user’s context. However, existing context-aware techniques have limited support for dynamic adaption to newly added context types (e.g., location, time and activity). Due to the diversity of user’s environment, the available context types may change over time. It is challenging to anticipate a complete set of context types while we design a context aware system. In this paper, we propose a context modeling approach which can dynamically handle various context types and values. More specifically, we use ontologies to enhance the meaning of a user’s context values and automatically indentify the relations among different context values. Based on the relations among context values, we capture the potential services which the user might need. A case study is conducted to evaluate the effectiveness of our approach. The results show that our approach can use contexts to find users’ needs and recommend their desired services with high precision and recall.