The aim of this work was to study the efficiency problem of similarity search over Print-On-Demand (POD) Platform. A "user-product" relation graph was built by utilizing the purchasing relationship between user and product, the similarity between products was measured according to the structure of "user-product" relation graph. For improving the efficiency, we proposed a similarity search method, POD-Rank, which divided the computation process into 2 steps. In the first step, we computed the similarity between users in an off-line manner; and in the second step, we computed the similarity between the query and each candidate product based on user similarity in an online manner. For further reducing the response time of on-line query processing, we proposed an optimized online query processing algorithm by skipping the unnecessary accumulation operations on zero-values. The space cost and pre-computation time cost of POD-Rank were evidently lower than those of P-Rank with little effectiveness loss and short online query time. By adopting the 2-step similarity computation method, the time cost was significantly reduced, the computation time cost was only 0.03% of that of P-Rank, the size of similarity matrix was only 0.06% of that of P-Rank, and the effectiveness was close to that of P-Rank. This method can therefore be efficiently applied to processing of large datasets of POP platform.