01892nam a22001817a 4500 NUST 20260127102903.0 005.1,KHA Khan, Rabia 15088 Cross-Domain Recommendation using Deep Learning / Rabia Khan Rawalpindi, MCS (NUST), 2025 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. PhD Computer Software Engineering Thesis 132801 PhD CSE Thesis 132802 Supervised by Dr. Naima Iltaf 132895 ddc THE 615954 615954 0 0 ddc 0 0 MCS MCS THE 2026-01-27 0 005.1,KHA MCSPhD CSE-26 2026-01-27 2026-01-27 THE