Shared　gradients　are　extensively　utilized　for　safeguarding　the　privacy　of　training　data.　However,　an　increasing　body　of　research　is　uncovering　that　the　gradients　or　model　parameters　transmitted　in　distributed　systems　may　also　leak　users＇　private　information.　A　majority　of　these　studies　are　predicated　on　intercepting　the　data　transmitted　by　individual　users　to　derive　their　private　information.　A　smaller　portion　of　research　can　extrapolate　a　multitude　of　users＇　private　data　through　the　average　gradients　passed　by　the　server.　These　investigations　have　analyzed　information　leakage　in　image　and　text　domains,　yet　have　not　explored　the　leakage　issues　inherent　within　recommendation　systems　operating　in　distributed　environments.　Furthermore,　the　impact　of　batch　size　and　model　parameters　on　the　degree　of　leakage　has　not　been　sufficiently　analyzed.　This　paper　proposes　that　within　recommendation　systems,　it　is　feasible　to　infer　the　encapsulated　user　privacy　data　by　receiving　average　gradients　or　model　parameters　provided　by　the　server.　Additionally,　the　paper　evaluates　the　extent　to　which　various　parameters　within　the　system　impact　the　leakage　and　presents　corresponding　defensive　measures　while　assessing　their　efficacy.