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Sandeep Kumar Jha

dblp:99/10224 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Databases, data management, data science and information retrieval · 2Artificial intelligence and machine learning · 1

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
Information retrieval · 56% Recommender systems · 44%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems
neural recommendation		 0.922020
Deep Natural Language Processing for Search and Recommendation · SIGIR 2020
Deep Learning for Search and Recommender Systems in Practice · KDD 2020
Information retrieval › retrieval models
neural retrieval		 0.412020
Deep Natural Language Processing for Search and Recommendation · SIGIR 2020
Information retrieval
retrieval models		 0.412020
Deep Natural Language Processing for Search and Recommendation · SIGIR 2020
Information retrieval
personalized search		 0.112020
Deep Learning for Search and Recommender Systems in Practice · KDD 2020
Information retrieval
search engines		 0.112020
Deep Learning for Search and Recommender Systems in Practice · KDD 2020

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

deep learning · 1.3
YearPublicationVenuePosition
2020 Deep Learning for Search and Recommender Systems in Practice
abstract
In this talk, we will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way.
Zhoutong Fu, Huiji Gao, Weiwei Guo, Sandeep Kumar Jha, Jun Jia, Bo Long, Sida Wang 0002, Mingzhou Zhou
 KDD4
2020 Deep Natural Language Processing for Search and Recommendation
abstract
Search and recommender systems process rich natural language text data such as user queries and documents. Achieving high-quality search and recommendation results requires processing and understanding such information effectively and efficiently, where natural language processing (NLP) technologies are widely deployed. In recent years, the rapid development of deep learning technology has been proven successful for improving various NLP tasks, indicating their great potential of promoting search and recommender systems.
Bo Long, Jieping Ye, Zang Li, Huiji Gao, Sandeep Kumar Jha
 SIGIR5