Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision
Table 2: Predicting the next click among all possible hotels using cosine similarity of the vectors.
['Vector used in cosine similarity  [BOLD] Session-32', 'Vector used in cosine similarity  [BOLD] Session-32', '[BOLD] Hits@10 14.1', '[BOLD] Hits@100 49.1']
In this section, rather than using the model’s output probabilities to induce a ranking over hotels , we measure hits@k over the ranking induced using cosine similarity of the embedding vectors. This is useful in scenarios where it isn’t feasible to directly use the model’s probabilities. We show that using the enriched vectors one achieves the highest performance.