Traditional　recommendation　systems　often　rely　on　explicit　feedback,　such　as　user　ratings　and　favorites.　However,　this　approach　suffers　from　data　sparsity　and　inaccuracy　issues.　In　contrast,　implicit　feedback　data,　such　as　user　browsing　history　and　click　records,　is　much　richer.　Therefore,　this　paper　uses　implicit　feedback　data　as　the　basis　for　recommendations,　models　user　behavior　to　uncover　their　potential　interests,　and　transforms　them　into　vector　representations.This　paper　also　introduces　interactive　interest　modeling.　This　approach　models　user　interest　representations　based　on　implicit　feedback　user　behavior　sequences.　For　sequence　recommendation　problems,　we　use　a　Transformer　model　to　extract　deeper　features　from　interactive　interest　representations　and　use　an　attention　mechanism　to　capture　users＇　real-time　interests.　Experimental　results　on　Kaggle　datasets　demonstrate　that　this　method　outperforms　traditional　implicit　feedback　recommendation　methods　in　terms　of　accuracy　and　personalization.　©　2023　IEEE.