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Typical e-commerce platforms contain millions of products in the catalog.Users visit these platforms and enter search queries to retrieve their desiredproducts. Therefore, showing the relevant products at the top is essential forthe success of e-commerce platforms. We approach this problem by learning lowdimension representations for queries and product descriptions by leveraginguser click-stream data as our main source of signal for product relevance.Starting from GRU-based architectures as our baseline model, we move towards amore advanced transformer-based architecture. This helps the model to learncontextual representations of queries and products to serve better searchresults and understand the user intent in an efficient manner. We performexperiments related to pre-training of the Transformer based RoBERTa modelusing a fashion corpus and fine-tuning it over the triplet loss. Ourexperiments on the product ranking task show that the RoBERTa model is able togive an improvement of 7.8% in Mean Reciprocal Rank(MRR), 15.8% in Mean AveragePrecision(MAP) and 8.8% in Normalized Discounted Cumulative Gain(NDCG), thusoutperforming our GRU based baselines. For the product retrieval task, RoBERTamodel is able to outperform other two models with an improvement of 164.7% inPrecision@50 and 145.3% in Recall@50. In order to highlight the importance ofpre-training RoBERTa for fashion domain, we qualitatively compare alreadypre-trained RoBERTa on standard datasets with our custom pre-trained RoBERTaover a fashion corpus for the query token prediction task. Finally, we alsoshow a qualitative comparison between GRU and RoBERTa results for productretrieval task for some test queries.

Neural Search: Learning Query and Product Representations in Fashion E-commerce