EDBT 2026 Demo / reviewers in the wild / expert
Harikrishnan C
dblp:371/7350
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0003-3416-9261ORCID · 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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › interactive information retrieval › conversational information seeking
conversational search |
0.8 | 1 | 2024 | Augmenting Keyword-based Search in Mobile Applications Using LLMs · WSDM 2024 |
Information retrieval › web search
mobile search |
0.8 | 1 | 2024 | Augmenting Keyword-based Search in Mobile Applications Using LLMs · WSDM 2024 |
Information retrieval
retrieval models |
0.8 | 1 | 2024 | Augmenting Keyword-based Search in Mobile Applications Using LLMs · WSDM 2024 |
Information retrieval
search engines |
0.8 | 1 | 2024 | Augmenting Keyword-based Search in Mobile Applications Using LLMs · WSDM 2024 |
Methods — techniques the papers use, named apart from their topics
large language model · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Augmenting Keyword-based Search in Mobile Applications Using LLMsabstractSearch in mobile applications has traditionally been keyword driven and limited to simple queries, such as searching for product names, even when the apps support much richer, transactional experiences. On the other hand, search on the web has evolved into queries that are complex, objective-based and most often in natural language. The recent advances in Generative AI make it possible to bring the power of web-like, conversational searches into mobile applications. In this talk, we present the various problems, opportunities and challenges in harnessing LLMs to augment the traditional search experience in mobile applications. Harikrishnan C, Giridhar Sreenivasa Murthy, Kumar Rangarajan |
WSDM | 1 |