PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights
Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributePersuasivenessSimilar Papers 제목 키워드 기반
AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce
The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KG…
Recommendation SystemsInformation RetrievalKnowledge GraphsSeeking the Unfamiliar but Memorable: Conceptual Creativity as Meta-Learning
What does it mean to create a new concept, rather than retrieve a familiar one? Repeatedly sampling a generative model at the same prompt produces variations with similar styles and typical content. We propose that creat…
Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues
Research suggests that providing specific and timely feedback to human tutors enhances their performance. However, it presents challenges due to the time-consuming nature of assessing tutor performance by human evaluator…
ChatbotPrompt EngineeringSTaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Answering real-world complex queries, such as complex product search, often requires accurate retrieval from semi-structured knowledge bases that involve blend of unstructured (e.g., textual descriptions of products) and…
BenchmarkingRetrievalAdaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience for consumers. However, accurately pred…
AttributeLanguage ModelingLanguage ModellingProduct Recommendation