With the growing complex interactions between users and items on the Internet, the difficulty of representing them has increased in Recommender Systems (RSs). Existing methods propose to capture the complex user-item interactions that implicitly associate a user with other like-minded ones. However, the learned embeddings cannot efficiently capture the similar patterns underlying users’ behaviors. Moreover, the implicitly retrieved patterns cannot be readily accurate and interpretable. To this end, we propose to leverage both users' interacted items and item tags, so as to advocate for tag-enhanced recommendations. Specifically, we propose an adaptive tag selection mechanism to ensure the quality of tag representations, so as to profile users and learn tag-enhanced representations of users/items. Additionally, we introduce a spherical optimization strategy to fully exploit the increased representation capacity of the tag-enhanced recommendation framework. We conduct numerous experiments on four real-world datasets, where our proposed tag-enhanced framework for recommendation can bring consistent performance gains and averagely achieve a 12.54% improvement regarding both Recall and NDCG metrics.