Researchers have developed a new ensemble approach for generating alternative vacation rental property recommendations, significantly improving recall rates. This method combines item-based collaborative filtering with graph neural networks (GNNs), outperforming previous baselines by 14.8% in Recall@300. The GNN component alone shows substantial gains over shallow embeddings like Hotel2Vec, demonstrating its effectiveness in handling cold-start scenarios and discovering diverse alternatives. The study also highlights that improvements in the candidate generation stage positively impact downstream ranking quality, though the precise relationship requires further investigation. AI
IMPACT This research could lead to more effective property discovery in online marketplaces, improving user experience and conversion rates.
RANK_REASON The cluster contains two identical arXiv preprints detailing a new research methodology for recommendation systems.
Read on arXiv cs.IR (Information Retrieval) →
- arXiv
- graph neural network
- Hotel2Vec
- Recall@300
- Syed Mohammed Arshad Zaidi
- alphaXiv
- CatalyzeX
- cs.IR
- cs.LG
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
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