105 citations · 337 across the 46 of their papers we have counts for
9 papers · 1 filter
Identifying High Consideration E-Commerce Search Queries
Zhiyu Chen, Jason Choi, Besnik Fetahu +1
In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider…
Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce
Saar Kuzi, Shervin Malmasi
Consumers on a shopping mission often leverage both product search and information seeking systems, such as web search engines and Question Answering (QA) systems, in an iterative…
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers
Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida +5
Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate ite…
Question Suggestion for Conversational Shopping Assistants Using Product Metadata
Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi
Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generat…
Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces
Nikhita Vedula, Giuseppe Castellucci, Eugene Agichtein +2
Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting,…
Identifying Shopping Intent in Product QA for Proactive Recommendations
Besnik Fetahu, Nachshon Cohen, Elad Haramaty +3
Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question…