Exploring Implicit Hierarchical Structures for Recommender Systems
Authors: Suhang Wang, Jiliang Tang, Yilin Wang, Huan Liu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework. In Section 4, we show empirical evaluation with discussion. |
| Researcher Affiliation | Academia | Suhang Wang, Jiliang Tang, Yilin Wang and Huan Liu School of Computing, Informatics, and Decision Systems Engineering Arizona State University, USA {suhang.wang, jiliang.tang, yilin.wang.1, huan.liu}@asu.edu |
| Pseudocode | Yes | Algorithm 1 The Optimization Algorithm for the Proposed Framework HSR. |
| Open Source Code | Yes | The code can be downloaded from http://www.public.asu.edu/~swang187/ |
| Open Datasets | Yes | The experiments are conducted on two publicly available benchmark datasets, i.e., Movie Lens100K 4 and Douban 5. ... 4http://grouplens.org/datasets/movielens/ 5http://dl.dropbox.com/u/17517913/Douban.zip |
| Dataset Splits | No | The paper mentions "We random select x% as training set and the remaining 1 x% as testing set" but does not specify a separate validation dataset split. While it states parameters are determined via cross-validation, it doesn't describe the splits for it. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, specific library versions). |
| Experiment Setup | Yes | We only show results with p = 2 and q = 2, i.e., W X W (U1U2V2V1) with U1 Rn n1, U2 Rn1 d, V1 Rd m1, and V2 Rm1 m, since we have similar observations with other settings of p and q. We fix d to be 20 and vary the value of n1 as {100, 200, 300, 400, 500} and the value of m1 as {200, 400, 600, 800, 1000}. |