Bayesian Probabilistic Multi-Topic Matrix Factorization for Rating Prediction
Authors: Keqiang Wang, Wayne Xin Zhao, Hongwei Peng, Xiaoling Wang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |