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..
Improving Implicit Recommender Systems with View Data
Authors: Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, Jiajie Yu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% 28.4%. |
| Researcher Affiliation | Collaboration | Jingtao Ding1, Guanghui Yu1, Xiangnan He2, Yuhan Quan1, Yong Li1, Tat-Seng Chua2, Depeng Jin1 and Jiajie Yu3 1 Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University 2 School of Computing, National University of Singapore 3 Beibei Inc |
| Pseudocode | Yes | Algorithm 1: Fast VALS Learning algorithm. |
| Open Source Code | Yes | Our implementation is available at: https: //github.com/dingjingtao/View enhanced ALS. |
| Open Datasets | Yes | Tmall2: Tmall is the largest E-commerce platform in China. To make our results reproducible, we use a public benchmark released in IJCAI-2015 challenge3. The dataset is downloaded from https://tianchi.aliyun.com/ datalab/data Set.htm?id=5 |
| Dataset Splits | No | The paper uses a 'leave-one-out protocol' for testing but does not explicitly mention a separate validation split. Training is done on 'the remaining data'. |
| Hardware Specification | Yes | Our experiment on the same machine (Intel Xeon 2.10 GHz CPU) shows that the actual training time per iteration for VALS on Beibei dataset is about 75s. |
| Software Dependencies | No | No specific software versions (e.g., library names with version numbers) were provided. |
| Experiment Setup | Yes | We tuned the weight of missing data si. We tuned the learning rate εBPR. For regularization, we set λ as 0.001 for all methods for a fair comparison. Since the findings are consistent across the number of latent factors K, we report the results of K = 32 only. Our VALS has two parameters: {γ1, γ2}, which are the margin values... and ci that determines the weight of view data. To find the best setting for (γ, c0), we conduct a grid search over these two parameters... we fix γ and c0 according to the best performance evaluated by HR, i.e., γ =0.3, c0 =1.6 for Beibei and γ =3.5, c0 =0.5 for Tmall. |