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Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing. Of particular significance is the book category, where the author name(s) field poses a significant challenge. Indeed, books written by a given author (such as F. Scott Fitzgerald) might be listed with different authors names in a catalog due to abbreviations and spelling variants and mistakes, among others. To solve this problem at scale, we design a composite system involving open data sources for books as well as machine learning components leveraging deep learning-based techniques for natural language processing. In particular, we use Siamese neural networks for an approximate match with known author names, and direct correction of the provided authors name using sequence-to-sequence learning with neural networks. We evaluate this approach on product data from the e-commerce website Rakuten France, and find that the top proposal of the system is the normalized author name with 72% accuracy.
Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search lo
Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks. However, BERT cannot well support E-commerce related tasks due to the lack of two levels of domain knowledge, i.e., phrase-lev
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms onl
In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critica
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for In-session prediction for purchase intent and recommendations. The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session,