Besnik Fetahu

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.

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.

Aug 31, 2023 | amazon.science | Zhiyu Chen |Besnik Fetahu |Oleg Rokhlenko |Shervin Malmasi

Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencies, errors, and informal syntax or phrasing. This is a major challenge in QA, causing unanswered questions or irrelevant answers, and leading to bad user experiences. We analyze failed QA requests to identify core challenges: lexical gaps, proposition types, complex syntactic structure, and high specificity.

Aug 31, 2023 | amazon.science | Zhiyu Chen |Jason Choi |Besnik Fetahu |Oleg Rokhlenko

Customers interacting with product search en-gines are increasingly formulating information-seeking queries. Frequently Asked Ques-tion (FAQ) retrieval aims to retrieve common question-answer pairs for a user query with question intent. Integrating FAQ retrieval in product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support.