Recommender systems have become a fundamental service in most E-Commerce platforms. Recently, some efforts to extract multi-interests from users’ historical records have demonstrated superior performance. However, aside from historical records, the user profile contains rich semantic information for interest extraction and inherently regulates the user’s interests. Existing works mainly overlook that a user’s interests have: group influence; multi-level preference; and time relevance. To this end, a novel Enhanced User Profile-based Multi-interest Model (E-UPMiM) for recommendation is proposed to integrate historical records with enhanced user profiles with social relationship information to model users’ multi-interests effectively. By leveraging these multiple data sources, our model aims to provide users with more accurate and personalized recommendation. We propose to extract user preferences with three corresponding components: integrated input with enhanced and updated user profile with social relationship information to meet user’s grouping needs; multi-interest extraction module to obtain complex multiple interest representations; and time-aware ranking module to adjust the order of recommendation list dynamically. Extensive experiments on three public datasets show that E-UPMiM significantly outperforms state-of-the-art recommendation models. From both qualitative and quantitative perspectives, the results demonstrate improvements in recommendation accuracy, personalization, and robustness to changes in user preferences.