Searching, fast and slow, through product catalogs
Dayananda Ubrangala Juhi Sharma Sharath Kumar Rangappa Kiran R Ravi Prasad Kondapalli Laurent Boué
String matching algorithms in the presence of abbreviations, such as in Stock
Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In
this paper, we present a unified architecture for SKU search that provides both
a real-time suggestion system (based on a Trie data structure) as well as a
lower latency search system (making use of character level TF-IDF in
combination with language model vector embeddings) where users initiate the
search process explicitly. We carry out ablation studies that justify designing
a complex search system composed of multiple components to address the delicate
trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as
an example, we show how our system vastly outperforms, in all aspects, the
results provided by the default search engine. Finally, we show how SKU
descriptions may be enhanced via generative text models (using gpt-3.5-turbo)
so that the consumers of the search results may get more context and a
generally better experience when presented with the results of their SKU
search.