Name
Abstract | ||
|---|---|---|
Matching between two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielded immense success in speech recognition, computer vision, and natural language processing, significantly advancing state-of-the-art of these areas. In the IR community, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fact that deep matching models have gained significant progress in these areas, there are still many challenges to be addressed when applying these models to real IR scenarios. In this workshop, we focus on the applicability of deep matching models to practical applications. We aim to discuss the issues of applying deep matching models to production systems, as well as to shed some light on the fundamental characteristics of different matching tasks in IR. website : https://wsdm2019-dapa.github.io/index.html
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3289600.3291375 | WSDM |
Keywords | Field | DocType |
deep learning, information retrieval, matching, practical application | Question answering,Information retrieval,Computer science,Artificial intelligence,Deep learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5940-5 | 1 | 0.35 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixing Fan | 1 | 202 | 19.39 |
| Qingyao Ai | 2 | 537 | 28.11 |
| Zhaochun Ren | 3 | 511 | 31.69 |
| Liangjie Hong | 4 | 1312 | 54.89 |
| Dawei Yin | 5 | 866 | 61.99 |
| Jiafeng Guo | 6 | 1737 | 102.17 |