Semantic Product Search
Table 3. Tokenization Experiments with Average Pooling and 3 Part L2 Hinge Loss
['Tokenization', 'Recall', 'MAP', 'Matching NDCG', 'Matching MRR', 'Ranking NDCG', 'Ranking MRR']
We use average pooling and the 3-part L2 hinge loss. For each tokenization method, we select the top k terms by frequency in the training data. Unless otherwise noted, k was set to 125K, 25K, 64K, and 500K for unigrams, bigrams, character trigrams, and out-of-vocabulary (OOV) bins respectively. Adding bigrams improves matching performance as bigrams capture short phrase-level information that is not captured by averaging unigrams. For example, the unigrams for “chocolate milk” and “milk chocolate” are the same although these are different products. Additionally including character trigrams improves the performance further as character trigrams provide generalization and robustness to spelling errors. Adding OOV hashing improves the matching performance as it allows better generalization to infrequent or unseen terms, with the caveat that it introduces additional parameters. These models have the same number of parameters, but the model with OOV hashing performs better.