Domain-specific personalized recommendation algorithm is getting more popular nowadays. In particularly, item-item relationship (e.g. complementary good, substitute good) has already been considered in the development of recommendation algorithms. In terms of its potential application for sellers, the ability to notice actual item-item complementarity from data is of paramount importance, as it helps sellers to gain a market competitive advantage via designing better pricing strategies (e.g. bundling or pricing discount). For recommender systems, integrating algorithms with the item-item relationship is also more likely to generate better recommendation results. Therefore, how to mine item-item complementarity is a research problem deserving of study. Even though most existing methodologies leverage on co-occurrence relationship, yet, the recommendation accuracy might easily be adversely affected by noise data due to the complex dynamics in the online shopping environment. In light of the research on economics, the latent complementarity discovery model (LCDM) is proposed in an attempt to more accurately describe the item-item relationship from a different perspective. Specifically, complementarity discovery model (CDM) is firstly proposed based on cross-price elasticity of demand, which jointly utilizes item pricing and purchase history to discover item-item complementarity relationship. Comparing with existing mining methods based on item co-occurrence relationship, the proposed method yields 10.6% increase in user label consistency. Then, LCDM is constructed by integrating dual-item attention with item-item complementarity insight mined from CDM. Lastly, from the comparison experiments conducted on real-world dataset, LCDM has made a significant improvement in recommendation performance, in which there is a 54.4% and 125.8% increase in Recall@5 and NDCG@5 respectively.