Cross-Domain Recommendation using Deep Learning / (Record no. 615954)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01892nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260127102903.0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1,KHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Khan, Rabia |
| 9 (RLIN) | 15088 |
| 245 ## - TITLE STATEMENT | |
| Title | Cross-Domain Recommendation using Deep Learning / |
| Statement of responsibility, etc. | Rabia Khan |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Rawalpindi, |
| Name of publisher, distributor, etc. | MCS (NUST), |
| Date of publication, distribution, etc. | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xix, 170 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Recommender systemsplayacrucialroleinfilteringvastinformation,buttraditionalsingle-<br/>domain modelsstrugglewithdatasparsity,cold-startissues,andlimitedpersonalization.<br/>These challengesareamplifiedasuserpreferencesoftenspanmultipledomains,which<br/>single-domain recommendersfailtocapture.Toaddresstheseissues,thisthesisexplores<br/>cross-domain recommendation using Multi-Layer Perceptron's (MLPs)to model non-linear<br/>dependencies andaligndomain-specificfeatures,improvingpersonalizationandscalability.<br/>However,MLPsoverlookthegraph-structurednatureofuser-iteminteractions,limiting<br/>their abilitytomodelhigher-orderrelationships.Toovercomethis,aGraphConvolutional<br/>Network-basedframeworkisproposedtolearnbothdomain-specificandsharedembed-<br/>dings throughcollaborativegraphsignals.Additionally,tocapturefine-grainedpreferences,<br/>another modelisproposedthatintegratesmetapath-basedgraphswithtransformer-based<br/>reviewencoding.Thiscapturesopiniondynamicsfromreviewsandenhancesknowledge<br/>transfer insparsesettings.<br/>Finally,recognizingthelimitationsofratings-onlymodelsandbiasedsharedembeddings,<br/>a sentiment-awarecontrastivelearningframeworkisintroduced.Bydynamicallyadapt-<br/>ing userrepresentationsusingreviewsentiment,themodelachievesimprovedcross-domain<br/>personalization, mitigatesnegativetransfer,andsupportsaccuraterecommendationsacross<br/>heterogeneous domains like movies and books. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | PhD Computer Software Engineering Thesis |
| 9 (RLIN) | 132801 |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME | |
| Geographic name | PhD CSE Thesis |
| 9 (RLIN) | 132802 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervised by Dr. Naima Iltaf |
| 9 (RLIN) | 132895 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | Thesis | 01/27/2026 | 005.1,KHA | MCSPhD CSE-26 | 01/27/2026 | 01/27/2026 | Thesis |