TY  - BOOK
AU  - Khan, Rabia 
AU  - Supervised by Dr. Naima Iltaf
TI  - Cross-Domain Recommendation using Deep Learning
U1  - 005.1,KHA 
PY  - 2025///
CY  - Rawalpindi
PB  - MCS (NUST)
KW  - PhD Computer Software Engineering Thesis
KW  - PhD CSE Thesis
N1  - 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
ER  -