Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes
Authors: Hideaki Kim, Tomoharu Iwata, Yasuhiro Fujiwara, Naonori Ueda
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the proposed model to synthetic and real-world check-in data, and show that it performs well in the well-timed recommendation task. |
| Researcher Affiliation | Industry | Hideaki Kim, Tomoharu Iwata, Yasuhiro Fujiwara, Naonori Ueda Ueda Research Laboratory, NTT Communication Science Laboratories 2 4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan {kin.hideaki, iwata.tomoharu, fujiwara.yasuhiro, ueda.naonori}@lab.ntt.co.jp |
| Pseudocode | No | The paper describes algorithms using equations and prose, but does not include structured pseudocode or an algorithm block labeled as such. |
| Open Source Code | No | The paper does not provide any information about open-source code availability for the described methodology. |
| Open Datasets | Yes | From the real-world check-in datasets collected by Brightkite1 (Cho, Myers, and Leskovec 2011), we make two subsets comprising the visit logs in San Francisco (SF) and New York City (NY), denoted by SF and NY, respectively. 1https://snap.stanford.edu/data/loc-brightkite.html |
| Dataset Splits | Yes | For all four datasets, the first 80% visit logs for each user, denoted by Dfit, are used for model fitting, and the remaining 20%, denoted by Dtest, for model evaluation. Note that this represents per-user splitting. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper describes the methods used (e.g., Gibbs sampling, slice sampling) but does not list any specific software libraries or their version numbers used in the implementation. |
| Experiment Setup | Yes | We set the width parameter, d L, at 100 in this study... In the following experiment, M was set at 100... In practice, we set [T0, T1] = [zero, the maximum of observed intervals]... Lmax = 200 in this paper. |