  Recommender systems and search are both indispensable in facilitating
personalization and ease of browsing in online fashion platforms. However, the
two tools often operate independently, failing to combine the strengths of
recommender systems to accurately capture user tastes with search systems'
ability to process user queries. We propose a novel remedy to this problem by
automatically recommending personalized fashion items based on a user-provided
text request. Our proposed model, WhisperLite, uses contrastive learning to
capture user intent from natural language text and improves the recommendation
quality of fashion products. WhisperLite combines the strength of CLIP
embeddings with additional neural network layers for personalization, and is
trained using a composite loss function based on binary cross entropy and
contrastive loss. The model demonstrates a significant improvement in offline
recommendation retrieval metrics when tested on a real-world dataset collected
from an online retail fashion store, as well as widely used open-source
datasets in different e-commerce domains, such as restaurants, movies and TV
shows, clothing and shoe reviews. We additionally conduct a user study that
captures user judgements on the relevance of the model's recommended items,
confirming the relevancy of WhisperLite's recommendations in an online setting.
