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+are+pivotal+in+diverse+online+web+platforms+and+have+garnered+considerable+attention+in+recent+years.+However%252C+previous+studies+overlook+the+challenges+of+(1)+noisy+multi-modal+content%252C+(2)+noisy+user+feedback%252C+and+(3)+aligning+multi-modal+content+with+user+feedback.+In+order+to+tackle+these+challenges%252C+we+propose+Denoising+and+Aligning+Multi-modal+Recommender+System+(DA-MRS).+To+mitigate+multi-modal+noise%252C+DA-MRS+first+constructs+item-item+graphs+determined+by+consistent+content+similarity+across+modalities.+To+denoise+user+feedback%252C+DA-MRS+associates+the+probability+of+observed+feedback+with+multi-modal+content+and+devises+a+denoised+BPR+loss.+Furthermore%252C+DA-MRS+implements+Alignment+guided+by+User+preference+to+enhance+task-specific+item+representation+and+Alignment+guided+by+graded+Item+relations+to+provide+finer-grained+alignment.+Extensive+experiments+verify+that+DA-MRS+is+a+plug-and-play+framework+and+achieves+significant+and+consistent+improvements+across+various+datasets%252C+backbone+models%252C+and+noisy+scenarios.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0377cb9216a23b1fbe6654b297941c76%3frik%3dhCq7GC%252faNMs1sQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Recommender+System+Multi+Head+Model&FORM=IRPRST&ck=35E2A6577A32671A9AA12DE520A73AB1&selectedIndex=11&itb=0)
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