Due to the increasing number of items with a variety of descriptions for a product type, itemset retrieval is considered as an essential function for enhancing shopping experiences of customers in online malls. This paper considers an itemset retrieval problem to construct an itemset consisting of items belonging to the same product type against a query item in which a customer is interested. In contrast to the previous approaches that require additional prior information such as itemset memberships and the known number of itemsets, we propose a semi-supervised itemset retrieval model that can automatically find a target itemset for a query item based on two item features, namely textual description and price. Specifically, in order to precisely identify itemsets, the proposed model conditionally utilizes price feature of an item only when its textual description feature is relevant to that of a query item. Experiment results based on two real-world datasets show that the proposed model outperformed the other alternatives.