Continuous Training of Recommendation System for Airbnb Listings Using Graph Learning

Yun Hong Chan - Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
Kok Why Ng - Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
Su Cheng Haw - Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.
Naveen Palanichamy - Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.

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Recommender systems are getting increasingly important nowadays as they can boost user engagement and benefit businesses. However, there remain some unsolved problems. This paper will address two key performance issues. First, the limited ability to identify and leverage intrinsic relationships between data points. Second, the inability to adapt to new data. The first issue is proposed to be addressed through a Graph Neural Network (GNN) to curate better recommendations. GNN will be trained with Airbnb’s review data to utilize its outstanding expressive power to represent complex user-listing interactions at scale, followed by generating embeddings to compute the relevant recommendations to the users. With the generated embeddings, the recommender system will compute a recommendation list to every user based on the embedding similarity between the user and listings or the user’s first-ever reviewed listing and listings. The second issue is proposed to be resolved by incorporating Continuous Training. The proposed recommender system employs GraphSAGE with a customized Rating-Weighted Triplet Ranking Loss function, which outperformed unsupervised GraphSAGE. Offline simulation validated the recommender system's ability to learn from the latest data and improve over time. Overall, the proposed user-to-item (U2I) recommendation rating-weighted GraphSAGE substantially increased by 99.88% in hit-rate@5 and 98.15% in coverage. This offers an effective solution for enhancing the recommender system for Airbnb listings. This research validates the efficacy of GNN-based recommendations in capturing user-item relationships to aid in predicting relevant recommendations, thus significantly driving up the adoption of GNN-based recommender systems.


Recommender system; Graph Neural Network; Deep learning; Continuous Training

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