LANCER: A Lifetime-Aware News Recommender System
Authors: Hong-Kyun Bae, Jeewon Ahn, Dongwon Lee, Sang-Wook Kim
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using real-world news datasets (e.g., Adressa and MIND), we successfully demonstrate that state-of-the-art news recommendation models can get significantly benefited by integrating the notion of lifetime and LANCER, by up to about 40% increases in recommendation accuracy. |
| Researcher Affiliation | Academia | Hong-Kyun Bae1, Jeewon Ahn1, Dongwon Lee2, Sang-Wook Kim*1 1 Department of Computer Science, Hanyang University, South Korea 2 College of Information Sciences and Technology, The Pennsylvania State University, USA |
| Pseudocode | No | The paper describes the proposed approach and its components using natural language and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We conduct experiments on two popular realworld datasets: MIND (Wu et al. 2020) and Adressa (Gulla et al. 2017) as shown in Table 1. |
| Dataset Splits | No | For MIND, we randomly sampled 200K users click logs and then divide the training and test sets, following the previous studies (Wu et al. 2019a; Qi et al. 2021b; Wu, Wu, and Huang 2021) which adopted MIND for their evaluation (i.e., 6 days and 1 day for the training and test sets, respectively). For Adressa which contains the click logs from a total of 5 weeks, we used the 4th and 5th weeks as training and test sets, respectively. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU/CPU models, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper mentions using existing DL-based models like NRMS, LSTUR, NAML, CNE-SUE, Attention Network, CNN, or LSTM, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | While training news recommendation models, we employed 8 as the value of K in Eq. 4. Then, to evaluate the accuracy of news recommendation, we constructed test sets to have 20 negative news for a user s single positive news during the test period...Here, we set α to the value showing the best accuracy of recommendation for each model, respectively...In Figure 8, where x-axis denotes α ( 10) and y-axis indicates the accuracy from the corresponding metrics. Regardless of the metrics, the results with α=0.4, α=0.1, and α=0.2 show the best performances for NRMS, LSTUR, and NAML, respectively. |