Sequential and Diverse Recommendation with Long Tail
Authors: Yejin Kim, Kwangseob Kim, Chanyoung Park, Hwanjo Yu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction. |
| Researcher Affiliation | Collaboration | Yejin Kim1 , Kwangseob Kim2 , Chanyoung Park3 and Hwanjo Yu3 1University of Texas Health Science Center at Houston 2Kakao Corp. 3Pohang University of Science and Technology |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is accessible at https://github.com/yejinjkim/seq-div-rec for reproducibility. |
| Open Datasets | No | For training, we collect 2.2 million active users click logs during 8 days. The total number of articles is 263,016... We perform offline experiments and online A/B tests on users historical logs on clicking blog articles from a commercial blog platform, Kakao (https://brunch.co.kr). The dataset used is proprietary and not publicly available via a direct link or formal citation. |
| Dataset Splits | Yes | In the offline experiments, we split the data into 70% for training, 10% for validation, and 20% for test by user IDs. |
| Hardware Specification | No | The paper mentions running 'online A/B tests on our commercial blog platform' and 'on both mobile and desktop sites' but does not provide specific hardware details such as GPU/CPU models or memory specifications used for training or experiments. |
| Software Dependencies | No | The paper mentions using 'word2vec model' and 'adaptive subgradient optimizer' but does not specify the version numbers for these or any other software libraries, programming languages, or environments. |
| Experiment Setup | Yes | We set the size N of a recommendation list is set to 20. ... we found lemb = 900, lhid = 550 performs best. We set dropout rate as 0.1, mini-batch size as 1024, and the number of epochs as 20. We use adaptive subgradient optimizer. |