A Multi-Source Ensemble Approach to Candidate Generation for Alternative Vacation Rental Property Recommendations

Abstract:Alternative property recommendations play a critical role in vacation rental marketplaces, helping users discover relevant options when viewing a specific listing. However, generating high-quality candidate alternatives presents unique challenges: heterogeneous inventory, geographic constraints, rapid availability changes, and long-tail property distributions. We present a comprehensive study of candidate generation (CG) approaches for vacation rental alternatives, comparing collaborative filtering, shallow embeddings, and graph neural network (GNN) methods. Our experiments on a large-scale vacation rental platform (over 2M active properties) show that a hybrid architecture combining item-based collaborative filtering with GNN-based retrieval improves Recall@300 by 14.8% over the strongest baseline, by leveraging the complementary strengths of the two sources: collaborative filtering excels at early recall for properties with rich interaction history, while GNNs discover diverse, non-obvious alternatives and handle cold-start scenarios more effectively. As a component result, GNN-based embeddings alone substantially outperform shallow Hotel2Vec embeddings (48-68% relative recall improvement across K), motivating their inclusion in the ensemble. Crucially, we examine how CG-stage gains carry through to the downstream ranking stage, and find that a stronger candidate pool yields higher downstream ranking quality, though attributing this effect cleanly is complicated by the coupling between candidate generation and ranker training. This recall-conversion gap is an important consideration for practitioners deploying new retrieval methods in two-stage recommendation systems.

Accepted at RecTour 2026, Workshop on Recommenders in Tourism, co-located with the 20th ACM Conference on Recommender Systems (RecSys 2026). To appear in CEUR Workshop Proceedings

Machine Learning (cs.LG); Information Retrieval (cs.IR)

arXiv:2609.05748 [cs.LG]

(or arXiv:2609.05748v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.05748 arXiv-issued DOI via DataCite (pending registration)