Khan, Rabia creator Supervised by Dr. Naima Iltaf text Rawalpindi MCS (NUST) 2025 monographic xix, 170 p Recommender 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. Rabia Khan PhD Computer Software Engineering Thesis PhD CSE Thesis 005.1,KHA 20260127102903.0