Discrete Trust-aware Matrix Factorization for Fast Recommendation
Authors: Guibing Guo, Enneng Yang, Li Shen, Xiaochun Yang, Xiaodong He
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency. |
| Researcher Affiliation | Collaboration | Guibing Guo1 , Enneng Yang1 , Li Shen2 , Xiaochun Yang1 and Xiaodong He3 1Northeastern University, China 2Tencent AI Lab, China 3JD AI Research, China |
| Pseudocode | Yes | Algorithm 1 Discrete Trust-aware Matrix Factorization Input: S: ratings matrix; Γ: trusts matrix; r: code length Output: user,item binary code:B, D 1: Initialization: B, W, X, Z Rr m and D, Y Rr n 2: while not converge do 3: for i {1, . . . , m} do 4: repeat 5: use Eq.(3) to update Bi bit by bit (r bits in total) 6: until bit converge 7: end for 8: for j {1, . . . , n} do 9: repeat 10: use Eq.(4) to update Dj bit by bit (r bits in total) 11: until bit converge 12: end for 13: for k {1, . . . , m} do 14: repeat 15: use Eq.(5) to update Wk bit by bit (r bits in total) 16: until bit converge 17: end for 18: update X by Eq.(6) 19: update Y by Eq.(7) 20: update Z by Eq.(8) 21: end while 22: return B, D to evaluate |
| Open Source Code | No | The paper does not explicitly provide a link to the source code or state that the code is publicly available. |
| Open Datasets | Yes | Two real-world datasets are used in our experiments, namely Epinions2 and Douban3. 2http://www.trustlet.org/wiki/Epinions dataset 3https://www.cse.cuhk.edu.hk/irwin.king.new/pub/data/douban |
| Dataset Splits | No | The paper states 'for each user, we randomly selected 50% as training data and the rest as test data.' It does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for the experiments. It only mentions 'The experiments are executed on Douban due to its greater size.' |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | Parameter settings The parameters of all the methods are either determined by empirical study or suggested by the original paper. Specifically, for BCCF, we tune hyper-parameters λ within [10 4, . . . , 10 2]. The hyper-parameters α and β of DCF are tuned with [10 4, . . . , 102]. For Trust MF, we adopt the parameter settings recommended by the authors: λ = 0.001 and λT = 1. For DTMF proposed in this paper, we search α, β, γ, and λ from [10 4, . . . , 103]. |