Browsing Regularities in Hedonic Content Systems
Authors: Ping Luo, Ganbin Zhou, Jiaxi Tang, Rui Chen, Zhongjie Yu, Qing He
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper studies common regularities of browsing behaviors in these systems, based on a large data set of user logs. We found that despite differences in visit time and user types, the distribution over browsing length for a visit can be described by the inverse Gaussian form with a very high precision. It indicates that the choice threshold model of decision making on continuing browsing or leave does exist. Also, We found that the stimulus intensity, in terms of the amount of recent enjoyed items, affects the probability of continuing browsing in a curve of inverted-U shape. We discuss the possible origin of this curve based on a proposed Award-Aversion Contest model. This hypothesis is supported by the empirical study, which shows that the proposed model can successfully recover the original inverse Gaussian distribution for the browsing length. |
| Researcher Affiliation | Academia | Ping Luo,1 Ganbin Zhou,1,2 Jiaxi Tang,3 Rui Chen,4 Zhongjie Yu,5 Qing He1 1Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. {luop@ict.ac.cn} 2University of Chinese Academy of Sciences, Beijing, China. 3School of Computing Science, Simon Fraser University, Cannada. 4School of Information, University of Michigan, USA. 5Deparment of Statistics and Finance, University of Science and Technology of China, China. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology or links to code repositories were found in the paper. |
| Open Datasets | No | The paper uses user logs collected from "Wallpapers Plus for i OS 8". It describes the data: "Totally, we have the user logs for the 41 days from Nov. 4 to Dec. 14, 2014. After the data preprocessing, we have 1,545,950 sequences for the whole analysis in the following." However, no concrete access information (link, DOI, repository, or citation for public availability) for this collected dataset is provided. |
| Dataset Splits | No | No explicit train/validation/test dataset splits, specific percentages, absolute counts, or cross-validation setup details were found. The paper describes fitting a distribution to the data and testing the quality of fit, but does not provide details on data partitioning for model validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments) used for running the experiments or analysis were found in the paper. |
| Software Dependencies | No | No specific ancillary software details with version numbers (e.g., programming languages, libraries, or solvers with their versions) were found. The paper mentions the 'simplex search method', but not the software used for its implementation or other dependencies. |
| Experiment Setup | No | No specific experimental setup details, such as concrete hyperparameter values (e.g., learning rates, batch sizes, epochs), optimizer settings, or system-level training configurations, were found in the main text of the paper. |