In a distributed eMarketplace, recommended product ontologies are required for trading between buyers and sellers. Conceptual clustering can be employed to build dynamic recommended product ontologies. Traditional methods of conceptual clustering (e.g. COBWEB or Cluster/2) do not take heterogeneous attributes of a concept into account. Moreover, the result of these methods is clusters other than recommended concepts. A center recommendation clustering algorithm is provided. According to the values of heterogeneous attributes, recommended product names can be selected at the clusters, which are produced by this algorithm. This algorithm can also create the hierarchical relations between product names. The definitions of product names given by all participants are collected in a distributed eMarketplace. Recommended product ontologies are built. These ontologies include relations and definitions of product names, which come from different participants in the distributed eMarketplace. Finally a case is given to illustrate this method. The result shows that this method is feasible. In a distributed eMarketplace, recommended product ontologies are required for trading between buyers and sellers. Conceptual clustering can be employed to build dynamic recommended product ontologies. Traditional methods of conceptual clustering (eg COBWEB or Cluster / 2) do not take heterogeneous attributes of a concept According to the values ​​of heterogeneous attributes, recommended product names can be selected at the clusters, which are produced by this algorithm. The algorithm can also create the hierarchical relations between product names. The definitions of product names given by all participants are collected in a distributed eMarketplace. Recommended on onologies are built. These ontologies include relations and definitions of product names, which come from different participants in the distributed eMarketplace. Fin ally a case is given to illustrate this method. The result shows that this method is feasible.