Amir Sabet Sarvestani

dblp:266/2900 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved

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

Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 2

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
2 papers
Data mining · 77% Information retrieval · 23%
Computer graphics and multimedia
2 papers
Visualization and visual analytics · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
competitive analysis
0.922020
Clarity: Data-Driven Automatic Assessment of Product Competitiveness · AAAI 2020
Data-Driven Ranking and Visualization of Products by Competitiveness · AAAI 2020
Visualization and visual analytics › interactive visualization
interactive dashboard
0.412020
Clarity: Data-Driven Automatic Assessment of Product Competitiveness · AAAI 2020
Visualization and visual analytics
interactive visualization
0.412020
Data-Driven Ranking and Visualization of Products by Competitiveness · AAAI 2020
Natural language and speech › Information extraction and text analysis
sentiment analysis
0.112020
Clarity: Data-Driven Automatic Assessment of Product Competitiveness · AAAI 2020
Information retrieval › search engines › semantic search › entity retrieval › entity ranking
product ranking
0.112020
Data-Driven Ranking and Visualization of Products by Competitiveness · AAAI 2020
Information retrieval
ranking
0.112020
Data-Driven Ranking and Visualization of Products by Competitiveness · AAAI 2020

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

unsupervised learning · 1.3sentiment analysis · 1.3content volume analysis · 1.3data-driven ranking · 0.9
YearPublicationVenuePosition
2020 Data-Driven Ranking and Visualization of Products by Competitiveness
Sheema Usmani, Mariana Bernagozzi, Michelle Morales, Amir Sabet Sarvestani, Biplav Srivastava
AAAI5
2020 Clarity: Data-Driven Automatic Assessment of Product Competitiveness
abstract
Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through an immense amount of online and offline content, aiming to discover what their competitors are doing in the marketplace to understand what type of threat they pose to their business' financial well-being. Currently, this process is time and labor-intensive, slow and costly. This paper presents Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment in the large IT company, IBM. Clarity has been running for more than a year and is used by over 1,500 people to perform over 160 competitive analyses involving over 800 products. The system considers multiple factors from a collection of online content: numeric ratings by online users, sentiments of reviews for key product performance dimensions, content volume, and recency of content. The results and explanations of factors leading to the results are visualized in an interactive dashboard that allows users to track their product's performance as well as understand main contributing factors. Its efficacy has been tested in a series of cases across IBM's portfolio which spans software, hardware, and services.
Sheema Usmani, Mariana Bernagozzi, Michelle Morales, Amir Sabet Sarvestani, Biplav Srivastava
AAAI5