User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation
Authors: Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation. |
| Researcher Affiliation | Collaboration | 1Department of Electronic Engineering & BNRist, Tsinghua University, Beijing 100084, China 2Microsoft Research Asia, Beijing 100080, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks with explicit labels like 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Our experiments are conducted on a large-scale public news recommendation dataset named MIND [Wu et al., 2020c], which contains the news impression logs of 1 million users from Microsoft News4 in 6 weeks (from Oct. 12 to Nov. 22, 2019). The samples in the last week are reserved for test, and those in the first 5 weeks are used for training and validation. The detailed statistics of this dataset are shown in Table 1. |
| Dataset Splits | Yes | The samples in the last week are reserved for test, and those in the first 5 weeks are used for training and validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific computing platforms) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like GAT, Glove, and Adam, but does not provide specific version numbers for these or any other ancillary software dependencies. |
| Experiment Setup | Yes | The number of HG-Pool layers is 2. Adam [Kingma and Ba, 2015] is used as the optimizer. The hyperparameters are tuned on the validation set. Each experiment is repeated 5 times. Following [Wu et al., 2019c], for each clicked news we randomly select P nonclicked news that are displayed in the same impression to build training samples. The loss function for model training is formulated as follows: i=1 log( exp(yi) exp(yi) + PP j=1 exp(yi,j) ), where S is the training set, yi and yi,j denote the predicted click score of the i-th clicked sample and its associated j-th non-clicked sample respectively. |