Shervin Malmasi

Oct 21, 2024 | amazon.science | Zhiyu Chen |Jason Choi |Besnik Fetahu |Shervin Malmasi

In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider the task of automatically identifying such High Consideration (HC) queries. Detecting such missions or searches enables e-commerce sites to better serve user needs through targeted experiences such as curated QA widgets that help users reach purchase decisions.

Jul 12, 2024 | arxiv.org | Shervin Malmasi

Jul 8, 2024 | amazon.science | Marcus Collins |Eugene Agichtein |Oleg Rokhlenko |Shervin Malmasi

Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated LLM chatbots, dialogue managers, response rankers, and recommenders. The problem is worse for multi-modal interactions in realistic conversational product search and recommendation.

Jan 18, 2024 | arxiv.org | Besnik Fetahu |Oleg Rokhlenko |Shervin Malmasi

Dec 6, 2023 | amazon.science | Besnik Fetahu |Zhiyu Chen |Oleg Rokhlenko |Shervin Malmasi

E-commerce product catalogs contain billions of items. Most products have lengthy titles, as sellers pack them with product attributes to improve retrieval, and highlight key product aspects. This results in a gap between such unnatural product titles and how customers refer to them. It also limits how e-commerce stores can use these seller-provided titles for recommendation, QA, or review summarization.