Learning　how　to　represent　users　based　on　historical　interactions　is　a　crucial　problem　for　recommender　systems.　Unavoidable　noise　in　interactions　and　long-tail　items　composed　of　a　large　number　of　unpopular　items　bring　more　challenges　for　learning　better　user　representations　and　still　limit　the　performance　of　existing　models.　Aiming　to　design　a　simple　model　that　can　alleviate　both　the　noise　problem　and　the　long-tail　item　problem,　we　propose　a　Hybrid　Normalization　strategy　via　feature　statistics　for　Collaborative　Filtering　(HyNCF).　After　each　user　is　represented　by　his/her　interacted　items,　the　feature　statistics　of　a　target　user　are　mixed　with　that　of　another　randomly　sampled　user.　In　addition,　the　uncertainty　estimation　of　the　target　user＇s　feature　statistics　is　calculated　by　a　Gaussian　sampling　technique.　Both　kinds　of　improved　feature　statistics　are　separately　used　to　normalize　the　target　user＇s　embedding,　and　then　normalized　embeddings　are　aggregated　to　generate　two　representations　of　the　user.　Based　on　the　fusion　of　the　two　representations,　the　cosine　contrastive　loss　is　used　to　train　HyNCF.　The　effectiveness　of　the　proposed　model　is　evaluated　on　five　benchmark　datasets.