000			01892nam a22001817a 4500
003			NUST
005			20260127102903.0
082			_a005.1,KHA
100			_aKhan, Rabia _915088
245			_aCross-Domain Recommendation using Deep Learning / _cRabia Khan
260			_aRawalpindi, _bMCS (NUST), _c2025
300			_axix, 170 p
505			_aRecommender systemsplayacrucialroleinfilteringvastinformation,buttraditionalsingle- domain modelsstrugglewithdatasparsity,cold-startissues,andlimitedpersonalization. These challengesareamplifiedasuserpreferencesoftenspanmultipledomains,which single-domain recommendersfailtocapture.Toaddresstheseissues,thisthesisexplores cross-domain recommendation using Multi-Layer Perceptron's (MLPs)to model non-linear dependencies andaligndomain-specificfeatures,improvingpersonalizationandscalability. However,MLPsoverlookthegraph-structurednatureofuser-iteminteractions,limiting their abilitytomodelhigher-orderrelationships.Toovercomethis,aGraphConvolutional Network-basedframeworkisproposedtolearnbothdomain-specificandsharedembed- dings throughcollaborativegraphsignals.Additionally,tocapturefine-grainedpreferences, another modelisproposedthatintegratesmetapath-basedgraphswithtransformer-based reviewencoding.Thiscapturesopiniondynamicsfromreviewsandenhancesknowledge transfer insparsesettings. Finally,recognizingthelimitationsofratings-onlymodelsandbiasedsharedembeddings, a sentiment-awarecontrastivelearningframeworkisintroduced.Bydynamicallyadapt- ing userrepresentationsusingreviewsentiment,themodelachievesimprovedcross-domain personalization, mitigatesnegativetransfer,andsupportsaccuraterecommendationsacross heterogeneous domains like movies and books.
650			_aPhD Computer Software Engineering Thesis _9132801
651			_aPhD CSE Thesis _9132802
700			_aSupervised by Dr. Naima Iltaf _9132895
942			_2ddc _cTHE
999			_c615954 _d615954