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..
Explainable Recommendation through Attentive Multi-View Learning
Authors: Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie3622-3629
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability. |
| Researcher Affiliation | Collaboration | 1Peking University, EMAIL 2Microsoft Research Asia, EMAIL |
| Pseudocode | No | The paper describes algorithms (e.g., dynamic programming) but does not present them in structured pseudocode blocks or labeled algorithm figures. |
| Open Source Code | No | Footnote 4 links to a CSV file of explanations, not the source code for the methodology described in the paper. No other explicit statement about open-sourcing the code is provided. |
| Open Datasets | Yes | We use three datasets from different domains for evaluation. Table 1 summarizes the statistics of the datasets. Toys and Games is the part of the Amazon dataset2 that focuses on Toys and Games. ... Digital Music is also from the Amazon 5-core dataset. ... Yelp consists of restaurant reviews from Yelp Challenge 20183. ... 2http://jmcauley.ucsd.edu/data/amazon 3https://www.yelp.com/dataset/challenge |
| Dataset Splits | Yes | We randomly split the dataset into training (70%), validation (15%) and test (15%) sets. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the "Adam optimizer (Kingma and Ba 2014)" but does not provide version numbers for any software dependencies, libraries, or programming languages used. |
| Experiment Setup | Yes | The number of latent factors k for algorithms is searched in [8,16,32,64,128]. After parameter tuning, we set k = 8 for NMF, PMF and HFT, and k = 16 for SVD++. We set k = 32 for EFM, CKE, Deep Co NN, NARRE and DEAML. We set d1, d2, d3, λv, and λa to 20, 10, 10, 10.0, and 3.0, respectively. |