Traffic Shaping in E-Commercial Search Engine: Multi-Objective Online Welfare Maximization
Authors: Liucheng Sun, Chenwei Weng, Chengfu Huo, Weijun Ren, Guochuan Zhang, Xin Li574-581
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also run experiments on both synthetic and real-world datasets to validate our algorithms. extensive experiments are conducted. The simulation results on a synthetic dataset validate the effectiveness of our proposed algorithm and the A/B testing results in the real-world production environment exhibit the effectiveness and practicability of the method. |
| Researcher Affiliation | Collaboration | Liucheng Sun1,2, Chenwei Weng1, Chengfu Huo1, Weijun Ren1, Guochuan Zhang2, Xin Li1 1Alibaba Group , 2Zhejiang University liucheng.slc@alibaba-inc.com, wengchenwei.pt@alibaba-inc.com, chengfu.huocf@alibaba-inc.com afei@alibaba-inc.com, zgc@zju.edu.cn, xin.l@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1: Greedy Traffic Splitting Algorithm, Algorithm 2: Probabilistic Balance Algorithm, Algorithm 3: Improved Traffic Splitting Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper uses a "synthetic dataset" for simulation, which is generated, and a "real-world production environment" (Alibaba's e-commerce search engine) for A/B testing, which is proprietary. There is no indication of public availability or access for these datasets. |
| Dataset Splits | No | The paper describes a synthetic dataset generation process and A/B test group splitting, but it does not specify explicit training, validation, or test dataset splits (e.g., "80/10/10 split" or specific sample counts for each split). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | For each query j, 200 randomly sampled items are retrieved for ranking, and bj is sampled from a uniform distribution U(3, 50). For each item-query pair (i, j), the relevance score rij is drawn from the beta distributions Beta(3, 2) and Beta(2, 3); the click probabilities are drawn from the uniform distributions U(0.1, 0.3) and U(0, 0.2); the conversion probabilities follow the uniform distributions U(0, 0.01) and U(0, 0.005) for mature and new items respectively... We set |Ic| = 1, 000 and |Is| = 1, 000... The target numbers of clicks are 18 and 2 for mature and new items respectively. We fix p1 = 0.9... we change the value of p2 from 0 to 0.1 with the step size 0.01 and p3 = 1 p1 p2. We set p1 = 0.96, p2 = 0.02, p3 = 0.02 and the experiment lasts for a week. |