Product Search refers to the application of artificial intelligence (AI) techniques to improve the process of searching for and retrieving relevant products from a database or online catalog. This is particularly prevalent in e-commerce platforms where users are searching for specific items, and the goal is to enhance the accuracy and efficiency of product recommendations. The goal is to create a more intelligent and user-friendly search experience. By understanding user intent, providing personalized recommendations, and adapting to changing patterns, machine learning enhances the efficiency of product searches, leading to increased user satisfaction and improved business outcomes for e-commerce platforms.

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Multi-Faceted Knowledge-Driven Pre-training for Product Representation Learning

Multi-Faceted Knowledge-Driven Pre-training for Product Representation Learning As a key component of e-commerce computing, product representation learning (PRL) provides benefits for a variety of applications, including product matching, search, and categorization. The existing PRL approaches have poor language understanding ability due to their inability to capture contextualized semantics. In addition, the learned representations by existing methods are not easily transferable to new products. Inspired by the recent advance of pre-trained language models (PLMs), we make the attempt to adapt PLMs for PRL to mitigate the above issues. In this article, we develop KINDLE, a Knowledge-drIven pre-trainiNg framework for proDuct representation LEarning, which can preserve the contextual semantics and multi-faceted product knowledge robustly and flexibly. Specifically, we first extend traditional one-stage pre-training to a two-stage pre-training framework and exploit a deliberate knowledge encoder to ensure a smooth knowledge fusion into PLM. In addition, we propose a multi-objective heterogeneous embedding method to represent thousands of knowledge elements. This helps KINDLE calibrate knowledge noise and sparsity automatically by replacing isolated classes as training targets in knowledge acquisition tasks. Furthermore, an input-aware gating network is proposed to select the most relevant knowledge for different downstream tasks. Finally, extensive experiments have demonstrated the advantages of KINDLE over the state-of-the-art baselines across three downstream tasks.