Prod2Query: Solving the Problem of Cold Start for E-Commerce Using Generative Language Modeling

Fedor Krasnov

Abstract


Large online marketplaces introduce thousands of new items daily. In order to purchase a new item, users must locate it through a search function, and search engines now predominantly utilize behavioral indicators when indexing products, such as purchasing, clicking, and viewing items, which are unavailable for new products. This constitutes the issue of the "cold start" of sales. With the advent of generative language models, it is now feasible to train a language model on user behavior to generate search queries for novel products. Consequently, a collection of synthetic behavioral data for novel items is generated, which may be utilized to train a search engine. The primary aim of this study is to assess the extent to which the autonomous indicators of a search engine trained on such synthetic data for novel products improve. Prod2Query is founded on the architecture of an Encoder-Decoder model, based on BERT transformers. As a result of testing new products using the Prod2Query system, an indicator for the new product extraction model was obtained, with an mAP@12 score of 77.2%. This score is on par with state-of-the-art (SOTA) models, indicating that the "cold start" problem can be effectively addressed based on signals from sales representatives when introducing new products. By generating synthetic search queries and training search models using these signals, it is possible to achieve high levels of accuracy in product retrieval.

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References


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