ScholarWorks@경상국립대학교

초록

Sequential recommendations seek to predict the next item a user will interact with by modeling historical behavior, yet most approaches emphasize either temporal dynamics or item relationships and thus miss how structural co-intents interact with dynamic preference shifts under realistic evaluation. IntentGraphRec introduces a dual-level framework that builds an intent graph from session co-occurrences to learn intent-aware item representations with a lightweight GNN, paired with a shift-aware Transformer that adapts attention to evolving preferences via a learnable fusion gate. To avoid optimistic bias, evaluation is performed with a leakage-free, full-catalog ranking protocol that forms prefixes strictly before the last target occurrence and scores against the entire item universe while masking PAD and prefix items. On MovieLens-1M and Gowalla, IntentGraphRec is competitive but does not surpass strong Transformer baselines (SASRec/BERT4Rec); controlled analyses indicate that late fusion is often dominated by sequence representations and that local co-intent graphs provide limited gains unless structural signals are injected earlier or regularized. These findings provide a reproducible view of when structural signals help, and when they do not, in sequential recommendations and offer guidance for future graph-sequence hybrids.

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