王泰翔

In the rapidly changing business environment, enterprises or individuals encounter more difficulties and challenges, and their encountered problems are also more complex and diverse. To sustain their competitive advantage, knowledge requirements need to be able to acquire knowledge quickly to solve their problems. Knowledge commerce (k-commerce) brings innovative thinking and profit models of knowledge assets management for enterprises. However, knowledge product is private, and search a desired knowledge product by hand form a lot of knowledge products is very time-consuming. Besides, a single knowledge product usually cannot satisfy the complex problem. Therefore, how to recommend the appropriate combination of knowledge products for satisfying customized knowledge requirement is an important issue. To overcome the above problem, this study develops a complementary knowledge product recommendation mechanism according to knowledge requesters’ requirements. At first, this study designs a structured representation model of knowledge requirement. Subsequently, this study proposes a similarity approach to match related knowledge products based on the knowledge requester’s requirement from the knowledge product ontology base. Finally, this study proposes a knowledge product combination approach using genetic algorithms to recommend the optimal combination of knowledge products according to knowledge product combination indicators. The mechanism effectively provides the desired knowledge for knowledge requester, thus facilitates successful knowledge products transactions.