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Darshan Nagaraja

dblp:398/7012 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0001-4086-2161ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
product discovery
0.912025
Navigating the Hype and Embracing the Hope: The Future of Generative AI in Retail Product Discovery · WSDM 2025

Methods — techniques the papers use, named apart from their topics

generative AI · 0.9
YearPublicationVenuePosition
2025 Navigating the Hype and Embracing the Hope: The Future of Generative AI in Retail Product Discovery
abstract
Generative AI is poised to revolutionize the retail industry, offering transformative potential to enhance customer experiences and operational efficiencies. Despite immense benefits -- such as personalized search, shopping assistance, product content enrichment, and dynamic recommendations -- the industry faces challenges distinguishing genuine value from overhyped applications. According to McKinsey & Company, Generative AI could unlock $240 billion to $390 billion in economic value for retailers, potentially increasing margins by up to 1.9 percentage points [1]. This paper navigates the delicate balance between the hype and the tangible promise of Generative AI in retail. We explore practical applications that deliver real value, critically examine overhyped use cases, and share strategies for successful AI integration. Readers will gain insights into effectively leveraging Generative AI while avoiding common pitfalls.
Darshan Nagaraja
WSDM1