Name
Abstract | ||
|---|---|---|
Machine learning techniques for developing industry-scale search engines have long been a prominent part of most domains and their online products. Search relevance algorithms are key components of products across different fields, including e-commerce, streaming services, and social networks. In this tutorial, we give an introduction to such large-scale search ranking systems, specifically focusing on deep learning techniques in this area. The topics we cover are the following: (1) Overview of search ranking systems in practice, including classical and machine learning techniques; (2) Introduction to sequential and language models in the context of search ranking; and (3) Knowledge distillation approaches for this area. For each of the aforementioned sessions, we first give an introductory talk and then go over an hands-on tutorial to really hone in on the concepts. We cover fundamental concepts using demos, case studies, and hands-on examples, including the latest Deep Learning methods that have achieved state-of-the-art results in generating the most relevant search results. Moreover, we show example implementations of these methods in python, leveraging a variety of open-source machine-learning/deep-learning libraries as well as real industrial data or open-source data. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3534678.3542632 | KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linsey Pang | 1 | 0 | 1.01 |
| Wei Liu | 2 | 468 | 37.36 |
| Keng-Hao Chang | 3 | 0 | 0.34 |
| Xue Li | 4 | 0 | 0.34 |
| Moumita Bhattacharya | 5 | 0 | 0.34 |
| Xianjing Liu | 6 | 0 | 0.34 |
| Stephen Guo | 7 | 2 | 1.73 |