  The more new features that are being added to smartphones, the harder it
becomes for users to find them. This is because the feature names are usually
short, and there are just too many to remember. In such a case, the users may
want to ask contextual queries that describe the features they are looking for,
but the standard term frequency-based search cannot process them. This paper
presents a novel retrieval system for mobile features that accepts intuitive
and contextual search queries. We trained a relevance model via contrastive
learning from a pre-trained language model to perceive the contextual relevance
between query embeddings and indexed mobile features. Also, to make it run
efficiently on-device using minimal resources, we applied knowledge
distillation to compress the model without degrading much performance. To
verify the feasibility of our method, we collected test queries and conducted
comparative experiments with the currently deployed search baselines. The
results show that our system outperforms the others on contextual sentence
queries and even on usual keyword-based queries.
