JUMP: a Jointly Predictor for User Click and Dwell Time

Authors: Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, Wenwu Ou

IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments demonstrate that JUMP outperforms state-of-the-art methods in both user click and dwell time prediction.
Researcher Affiliation Collaboration Tengfei Zhou1, Hui Qian1 , Zebang Shen1, Chao Zhang1,Chengwei Wang1, Shichen Liu2, Wenwu Ou2 1College of Computer Science and Technology, Zhejiang University, 2Searching Group of Alibaba Inc. {zhoutengfei,qianhui,shenzebang,zczju, rr}@zju.edu.cn, shichen.lsc@alibaba-inc.com, santong.oww@taobao.com
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any specific links to source code repositories or explicitly state that the code for the described methodology is open-source or publicly available.
Open Datasets Yes We conduct experiments on three publicly available datasets including Rec Sys15, CIKM16, and REDDIT.
Dataset Splits No The paper states: "we split all sessions of the datasets into 80% for training and 20% for testing." It does not explicitly mention a separate validation split or its size.
Hardware Specification Yes All the compared methods are performed on the same PC with i7-7820HK CPU, 16GB RAM, and GTX1080 GPU.
Software Dependencies No The paper mentions "All the compared methods are optimized by Adam" but does not provide specific version numbers for any software dependencies, such as programming languages, libraries, or frameworks (e.g., Python version, TensorFlow/PyTorch versions).
Experiment Setup Yes All the compared methods are optimized by Adam with the batch size set to 100. For all the methods in our comparison, the dimension of item embedding vectors is set to 100... For our model, we set σ to 10, c1 = 2000 and c2 = 30.