Item Recommendation for Emerging Online Businesses
Authors: Chun-Ta Lu, Sihong Xie, Weixiang Shao, Lifang He, Philip S. Yu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on realworld datasets demonstrate that our method significantly outperforms other state-of-the-art recommendation models in addressing item recommendation for emerging businesses. |
| Researcher Affiliation | Academia | Chun-Ta Lu, Sihong Xie, Weixiang Shao, Lifang He, and Philip S. Yu Department of Computer Science, University of Illinois at Chicago, IL, USA Institute for Computer Vision, Shenzhen University, China Institute for Data Science, Tsinghua University, Beijing, China. {clu29,sxie6,wshao4}@uic.edu, lifanghescut@gmail.com, psyu@uic.edu |
| Pseudocode | No | The paper describes methods and equations, but it does not contain a pseudocode block or an algorithm labeled as such. |
| Open Source Code | No | The paper does not mention providing access to its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our proposed recommendation model on two real-world datasets: Yelp1 and Epinions [Tang et al., 2012]. 1http://www.yelp.com/dataset challenge/ |
| Dataset Splits | Yes | For each target business, we conduct the experiments using 5-fold cross-validation: one fold is used as training data, the remaining folds are used as testing data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | For each method, we randomly initialize the latent factors and set the maximum number of iterations as 100. For RMGM, the latent dimensionality k is set as the number of clusters, the default choice for most kernel-based approaches. The sparsity tradeoff parameter is fixed as 0.1 for both datasets. We set the similarity tradeoff parameter λ = 1 as in [Yu et al., 2013; Luo et al., 2014] and tune the alignment tradeoff parameter β by searching the grid of {10 5, , 103}. |