How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead
This work addresses the need for better personalized search suggestions in eCommerce to enhance user experience and potentially increase sales, though it appears incremental as it builds on existing neural models with specific enhancements.
The paper tackles the problem of improving category facet suggestions in eCommerce type-ahead search by balancing precision and recall, introducing SessionPath, a neural network model that leverages session embeddings for personalization and predicts facets via probability distributions in taxonomy paths, achieving improvements over count-based and neural benchmarks in two partnering shops.
In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions. In this work, we present SessionPath, a novel neural network model that improves facet suggestions on two counts: first, the model is able to leverage session embeddings to provide scalable personalization; second, SessionPath predicts facets by explicitly producing a probability distribution at each node in the taxonomy path. We benchmark SessionPath on two partnering shops against count-based and neural models, and show how business requirements and model behavior can be combined in a principled way.