Authors - Md. Shahidul Islam, Hasina Islam Abstract - Cross-domain recommendations are imperative in the growing tourism industry and with the increasing means of communication. Preference drift, preference transfer, and unfamiliarity with places have an overbearing impact on recommender systems. Most approaches do not address geometric misalignment across domains, which is essential for cross-domain preference shift analysis in recommendation tasks. We propose Procrustes-Based Contextual Thompson Sampling (P-CTS) for Cross-Domain POI Recommendation, integrating adversarial domain-invariant learning, optimal geometric alignment via Procrustes transformation, and adaptive Thompson Sampling with sleeping bandit management. First, the embeddings are constructed to model the preference drift across the domains. Next, the Procrustes transformation aligns source and target embedding spaces via optimal rotation, scaling, and translation. In the last phase, we initialize Beta priors with similarity-weighted pseudo-counts derived from the aligned embeddings. The experiments on Gowalla and Foursquare across domains demonstrate 5.1% improvements in Precision@5 and 9.75% improvements in cold-start accuracy, suggesting an adaptive exploration-exploitation trade-off.
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