On　the　users＇　interaction　graph,　neighbors　have　been　widely　explored　in　the　embedding　function　of　collaborative　filtering　to　address　the　sparsity　issue.　However,　the　embedding　learning　models　are　highly　subject　to　the　following　pairwise　interaction　function　on　interest　prediction.　We　argue　that　the　core　of　personalized　recommendation　locates　interaction　rather　than　embeddings.　Distinct　from　the　sparse　pairwise　interactions,　there　are　a　large　amount　of　inherent　non-pairwise　signals　hidden　among　neighbors,　which　are　promising　for　interaction　learning.　In　this　work,　we　explore　the　active　effect　of　non-pairwise　neighbors　on　the　target　user-item　pair　and　propose　a　non-pairwise　collaborative　filtering　(NPCF)　model.　For　a　target　user-item　pair,　NPCF　mines　target-aware　CF　signals　of　neighbors　by　aggregating　both　pairwise　and　non-pairwise　CF　signals　of　the　target　for　a　target-specific　interaction　embedding.　Experiments　on　three　real-world　datasets　demonstrate　that　NPCF　outperforms　the　state-of-the-art　models　for　personalized　recommendation.　It　implies　NPCF　is　capable　of　learning　interactions　with　non-pairwise　neighbors.