Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Action Space Learning for Heterogeneous User Behavior Prediction
Authors: Dongha Lee, Chanyoung Park, Hyunjun Ju, Junyoung Hwang, Hwanjo Yu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that METAS outperforms the state-ofthe-art methods in predicting users heterogeneous actions, and its entity space represents the useruser and item-item similarities more clearly than the space trained by the other methods. |
| Researcher Affiliation | Academia | 1Pohang University of Science and Technology, Pohang, Republic of Korea 2University of Illinois at Urbana-Champaign, Urbana, IL, USA |
| Pseudocode | Yes | Algorithm 1: Hard triplet mining algorithm |
| Open Source Code | No | The paper states 'We implement METAS and CML using Tensorflow [Abadi et al., 2016] to run on GPU, and utilize the JAVA source code of SPTF released by [Yin et al., 2017].' This indicates they used existing open-source tools but does not provide a link or explicit statement about releasing their own implementation code for METAS. |
| Open Datasets | Yes | Tmall is the behavior dataset collected by Tmall of Alibaba, one of the biggest e-commerce platforms, and it is published by [Yin et al., 2017]. Taobao is also users-commodities behavior data on Alibaba s mobilecommerce platforms, publicly available from Ali Mobile Recommendation Algorithm Competition. |
| Dataset Splits | Yes | For each user, we leave out a single observed action per action type for testing, and use the rest for training. In our experiments, we leave out an additional observed action for a validation set. ... For each dataset, we tune the hyperparameters by using a grid search and use the optimal values that show the best H@10 on the validation set. |
| Hardware Specification | No | The paper states 'We implement METAS and CML using Tensorflow [Abadi et al., 2016] to run on GPU'. This is a general statement and does not specify any particular GPU model, CPU, or other hardware details. |
| Software Dependencies | No | The paper mentions 'Tensorflow [Abadi et al., 2016]' and 'Adam optimizer [Kingma and Ba, 2014]' but does not provide specific version numbers for these software dependencies. It also mentions 'JAVA source code of SPTF released by [Yin et al., 2017]'. |
| Experiment Setup | Yes | The MLP networks of METAS are equipped with a single layer, 50% dropout probability, and Re LU activation. For each dataset, we tune the hyperparameters by using a grid search and use the optimal values that show the best H@10 on the validation set. We set the margin size α = 2 and the mini-batch size (i.e., the number of triplets in a mini-batch) b = 200. ... We set D1 = D2 = D to reduce the model complexity, and investigate the performance changes with respect to the dimension size D with a range of {50, 100, 150, 200, 250}. ... In Algorithm 1, we set the candidate itemset size c to the size of mini-batch, and n = 3. |