  Typical e-commerce platforms contain millions of products in the catalog.
Users visit these platforms and enter search queries to retrieve their desired
products. Therefore, showing the relevant products at the top is essential for
the success of e-commerce platforms. We approach this problem by learning low
dimension representations for queries and product descriptions by leveraging
user click-stream data as our main source of signal for product relevance.
Starting from GRU-based architectures as our baseline model, we move towards a
more advanced transformer-based architecture. This helps the model to learn
contextual representations of queries and products to serve better search
results and understand the user intent in an efficient manner. We perform
experiments related to pre-training of the Transformer based RoBERTa model
using a fashion corpus and fine-tuning it over the triplet loss. Our
experiments on the product ranking task show that the RoBERTa model is able to
give an improvement of 7.8% in Mean Reciprocal Rank(MRR), 15.8% in Mean Average
Precision(MAP) and 8.8% in Normalized Discounted Cumulative Gain(NDCG), thus
outperforming our GRU based baselines. For the product retrieval task, RoBERTa
model is able to outperform other two models with an improvement of 164.7% in
Precision@50 and 145.3% in Recall@50. In order to highlight the importance of
pre-training RoBERTa for fashion domain, we qualitatively compare already
pre-trained RoBERTa on standard datasets with our custom pre-trained RoBERTa
over a fashion corpus for the query token prediction task. Finally, we also
show a qualitative comparison between GRU and RoBERTa results for product
retrieval task for some test queries.
