Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
Authors: Tao Zhuang, Wenwu Ou, Zhirong Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed online A/B test on a large e-commerce search engine. The results show that our method brings a 5% increase in GMV for the search engine over a strong baseline. |
| Researcher Affiliation | Industry | Tao Zhuang1, Wenwu Ou2, Zhirong Wang3 Taobao Search, Alibaba Group Holding Limited |
| Pseudocode | Yes | Algorithm 1 Beam search for Ranking with RNN model |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | Our training data for purchase probability estimation are from the query logs of Taobao Search. |
| Dataset Splits | No | The paper specifies a train/test split by time ('one day s log data for training and the next day s log data for test') but does not mention a distinct validation split. |
| Hardware Specification | No | The paper mentions performing 'online A/B test on Taobao Search' but does not specify any particular hardware components like CPU or GPU models used for training or inference. |
| Software Dependencies | No | The paper mentions using 'DNN', 'RNN', and 'LSTM' as model architectures but does not list any specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | In mi DNN model, the sizes of the three hidden layers are 50, 50, 30. In RNN models, the hidden vector size of LSTM is 50. The sizes of ai and posi in Equation (13) are 10 and 5 respectively. The beam sizes of our RNN models are set to 5 unless stated otherwise. |