Recommender　systems　play　a　crucial　role　in　providing　personalized　services　but　face　significant　challenges　from　data　sparsity　and　long-tail　bias.　Researchers　have　sought　to　address　these　issues　using　self-supervised　contrastive　learning.　Current　contrastive　learning　primarily　relies　on　self-supervised　signals　to　enhance　embedding　quality.　Despite　performance　improvement,　task-independent　contrastive　learning　contributes　limited　to　the　recommendation　task.　In　an　effort　to　adapt　contrastive　learning　to　the　task,　we　propose　a　preference　contrastive　learning　(PCL)　model　by　contrasting　preferences　of　user-items　pairs　to　model　users＇　interests,　instead　of　the　self-supervised　user-user/item-item　discrimination.　The　supervised　contrastive　manner　works　in　a　single　view　of　the　interaction　graph　and　does　not　require　additional　data　augmentation　and　multi-view　contrasting　anymore.　Performance　on　public　datasets　shows　that　the　proposed　PCL　outperforms　the　state-of-the-art　models,　demonstrating　that　preference　contrast　betters　self-supervised　contrast　for　personalized　recommendation.