Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Bayesian Probabilistic Multi-Topic Matrix Factorization for Rating Prediction
Authors: Keqiang Wang, Wayne Xin Zhao, Hongwei Peng, Xiaoling Wang
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,148 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,008 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on large real-world datasets demonstrate the effectiveness of the proposed model. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China 2School of Information, Renmin University of China, China |
| Pseudocode | Yes | Algorithm 1: The learning algorithm for BPMTMF. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate the models on the two publicly available movie datasets Movielens1 and Netflix2 described in Table 2. 1http://www.grouplens.org/ 2http://www.netflixprize.com/ |
| Dataset Splits | Yes | We randomly split the data into training set and testing set with the ratio 9 : 1. The final results are reported by the average of five such runs. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'librec toolkit' but does not specify any software versions for libraries or programming languages. |
| Experiment Setup | Yes | the number of dimensions for latent vectors, i.e., D, is set to 20 for all the methods. For BPMTMF and PMTMF, we empirically set the number of topics K to 20; Following [Griffiths and Steyvers, 2004], and β are set to 50 K and 0.01 respectively. Following [Salakhutdinov and Mnih, 2008], we initialize µ(k) 0 = D, W(k) 0 to the identity matrix and variance σ2 k = 2. As indicated in [Salakhutdinov and Mnih, 2008], the predictive accuracy becomes relatively stable when the iteration number is larger than 150 for BPMF. Hence, for BPMTMF, we discard 200 iterations for burn-in, and run another 150 iterations for sampling. |