E-commerce recommendation accuracy can be improved by mining patterns from other domains such as e-commerce and social media (Facebook), to predict purchase behaviours. Existing systems such as the GaoLinRec23, GaoChenLin, WangZhaoRec, employ Collective Matrix Factorization to jointly factorize user-item interaction matrices from both domains have contributed deeply. However, the assumption that specific product details are shared between these domains does not align with the real-world scenario. Major e-commerce and social media platforms, such as Amazon and Facebook, typically do not exchange granular product information. This disconnect poses a critical obstacle for existing recommendation systems in providing accurate suggestions for users starting with no observable e-commerce activity. This paper proposes a system called Facebook Data Cross Recommendation ’2023 (FD-CDR ’23), which uses the proposed MLTU (Mine Likes and Transactions per User) algorithm to extract Likes and purchase history of users from both domains, transforming them into itemsets. A modified association rule mining is then applied to uncover patterns of frequent co-occurrence between user Facebook post likes and e-commerce transactions as rules. It uses the proposed HARR (Hybrid Association Rule Recommendation) algorithm to match new user Facebook likes to, generate rules such as “Users who typically like cooking posts, buy cooking recipes” without needing to share products across platforms. Experimental results with precision and recall show that the proposed FD-CDR’23 system provides more accurate recommendations than the existing systems.