Name
Abstract | ||
|---|---|---|
In e-commerce search, vectorized matching is the most important approach besides lexical matching, where learning vector representations for entities (e.g., query, item, shop) plays a crucial role. In this work, we focus on vectorized search matching model for shop search in Taobao. Unlike item search, shop search is faced with serious behavior sparsity and long-tail problem. To tackle this, we take the first step to transfer knowledge from item search, i.e., leveraging items purchased under a query and the shops they belong to. Moreover, we propose a novel gated heterogeneous graph learning model (named GHL) to derive vector representations for entities. Both first-order and second-order proximity of queries and shops are exploited to fully mine the heterogeneous relationships. And to relieve long-tail phenomenon, we devise an innovative gated neighbor aggregation scheme where each type of entities (i.e., hot ones and long-tail ones) can benefit from the heterogeneous graph in an automatic way. Finally, the whole framework is jointly trained in an end-to-end fashion. Offline evaluation results on real-world data of Taobao shop search platform demonstrate that the proposed model outperforms existing graph based methods, and online A/B tests show that it is highly effective and achieves significant CTR improvements.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3412087 | 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 | 0 |
PageRank | References | Authors |
0.34 | 7 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xichuan Niu | 1 | 12 | 1.54 |
| bofang li | 2 | 8 | 2.12 |
| Chenliang Li | 3 | 590 | 39.20 |
| Rong Xiao | 4 | 70 | 7.49 |
| Haochuan Sun | 5 | 6 | 0.72 |
| Honggang Wang | 6 | 0 | 0.34 |
| Hongbo Deng | 7 | 861 | 41.00 |
| Zhenzhong Chen | 8 | 1244 | 101.41 |