RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search
This work addresses revenue optimization in e-commerce sponsored search, representing an incremental improvement over existing methods.
The authors tackled the problem of poor Revenue Per Mille (RPM) performance in sponsored search for heavy-tailed query distributions by proposing an RPM-oriented Query Rewriting Framework (RQRF), which significantly outperformed traditional baselines in large-scale real-world e-commerce traffic.
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM). Existing sponsored search models are all based on traditional statistical models, which have poor RPM performance when queries follow a heavy-tailed distribution. Here, we propose an RPM-oriented Query Rewriting Framework (RQRF) which outputs related bid keywords that can yield high RPM. RQRF embeds both queries and bid keywords to vectors in the same implicit space, converting the rewriting probability between each query and keyword to the distance between the two vectors. For label construction, we propose an RPM-oriented sample construction method, labeling keywords based on whether or not they can lead to high RPM. Extensive experiments are conducted to evaluate performance of RQRF. In a one month large-scale real-world traffic of e-commerce sponsored search system, the proposed model significantly outperforms traditional baseline.