Name
Title | ||
|---|---|---|
Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space |
Abstract | ||
|---|---|---|
Traditional Learning to Rank (LTR) models in E-commerce are usually trained on logged data from a single domain. However, data may come from multiple domains, such as hundreds of countries in international E-commerce platforms. Learning a single ranking function obscures domain differences, while learning multiple functions for each domain may also be inferior due to ignoring the correlations between domains. It can be formulated as a multi-task learning problem where multiple tasks share the same feature and label space. To solve the above problem, which we name Multi-Scenario Learning to Rank, we propose the Hybrid of implicit and explicit Mixture-of-Experts (HMoE) approach. Our proposed solution takes advantage of Multi-task Mixture-of-Experts to implicitly identify distinctions and commonalities between tasks in the feature space, and improves the performance with a stacked model learning task relationships in the label space explicitly. Furthermore, to enhance the flexibility, we propose an end-to-end optimization method with a task-constrained back-propagation strategy. We empirically verify that the optimization method is more effective than two-stage optimization required by the stacked approach. Experiments on real-world industrial datasets demonstrate that HMoE significantly outperforms the popular multi-task learning methods. HMoE is in-use in the search system of AliExpress and achieved 1.92% revenue gain in the period of one-week online A/B testing. We also release a sampled version of our dataset to facilitate future research.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3412713 | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
Virtual Event
Ireland
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-6859-9 | 2 |
PageRank | References | Authors |
0.39 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengcheng Li | 1 | 2 | 2.08 |
| Runze Li | 2 | 112 | 20.80 |
| Qing Da | 3 | 49 | 5.64 |
| Anxiang Zeng | 4 | 46 | 6.39 |
| Lijun Zhang | 5 | 980 | 63.68 |