Demo Data from EDBT 2026 / reviewers in the wild / expert
Hossein Entezari Zarch
dblp:304/2599
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-1976-1534ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 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 |
Recommender systems · 100% | |
| Artificial intelligence
1 paper |
Efficient and distributed learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
model compression |
1.0 | 1 | 2026 | HuffmanEmbed: Using Huffman Coding for Embedding Table Compression in Deep Learning Recommendation Models · SIGIR 2026 |
Recommender systems › model compression
embedding compression |
1.0 | 1 | 2026 | HuffmanEmbed: Using Huffman Coding for Embedding Table Compression in Deep Learning Recommendation Models · SIGIR 2026 |
Recommender systems
neural recommendation |
1.0 | 1 | 2026 | HuffmanEmbed: Using Huffman Coding for Embedding Table Compression in Deep Learning Recommendation Models · SIGIR 2026 |
Methods — techniques the papers use, named apart from their topics
huffman coding · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | HuffmanEmbed: Using Huffman Coding for Embedding Table Compression in Deep Learning Recommendation Models
Chaoyi Jiang, Abdulla Alshabanah, Hossein Entezari Zarch, Keshav Balasubramanian, Murali Annavaram |
SIGIR | 3 |