1. A recommendation method comprising: providing an ontology database comprising an ontology hierarchy structure with N hierarchy levels, wherein N is an integer, and each of the hierarchy levels comprises at least one entity; storing, through the ontology database, a plurality of j th user data respectively corresponding to a plurality of users, wherein each of the j th user data records at least one j th entity of the entities on a j th hierarchy level of the ontology hierarchy structure; generating a plurality of k th user data corresponding to the users according to the j th user data respectively, wherein each of the k th user data records at least one k th entity of the entities on a k th hierarchy level of the ontology hierarchy structure; clustering the k th user data; and recommending the entities in the ontology database to the users according to the clustering result, wherein the step of generating the k th user data corresponding to the users comprises: calculating a sparsity of the j th user data; and mapping the j th entity recorded in each of the j th user data to at least one of the k th entity recorded in each of the k th user data according to the sparsity of the j th user data; wherein a first calculating value is equal to a product of a quantity of the users and a quantity of the entities on the j th hierarchy level, a second calculating value is equal to a quantity of a sum of the j th entity recorded by each of the j th user data divided by the first calculating value, and the sparsity of the j th user data is equal to 1 subtracted by the second calculating value.
1. A recommendation method comprising: providing an ontology database comprising an ontology hierarchy structure with N hierarchy levels, wherein N is an integer, and each of the hierarchy levels comprises at least one entity; storing, through the ontology database, a plurality of j th user data respectively corresponding to a plurality of users, wherein each of the j th user data records at least one j th entity of the entities on a j th hierarchy level of the ontology hierarchy structure; generating a plurality of k th user data corresponding to the users according to the j th user data respectively, wherein each of the k th user data records at least one k th entity of the entities on a k th hierarchy level of the ontology hierarchy structure; clustering the k th user data; and recommending the entities in the ontology database to the users according to the clustering result, wherein the step of generating the k th user data corresponding to the users comprises: calculating a sparsity of the j th user data; and mapping the j th entity recorded in each of the j th user data to at least one of the k th entity recorded in each of the k th user data according to the sparsity of the j th user data; wherein a first calculating value is equal to a product of a quantity of the users and a quantity of the entities on the j th hierarchy level, a second calculating value is equal to a quantity of a sum of the j th entity recorded by each of the j th user data divided by the first calculating value, and the sparsity of the j th user data is equal to 1 subtracted by the second calculating value. 3. The recommendation method as claimed in claim 1 , wherein the step of mapping the j th entity recorded in each of the j th user data to at least one of the k th entity recorded in each of the k th user data comprises: determining whether the sparsity of the j th user data is greater than a j th threshold; and mapping the j th entity recorded in each of the j th user data to at least one of the k th entity recorded in each of the k th user data in a case that the sparsity of the j th user data is greater than the j th threshold.