
## STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang
Keywords: DMKM: Recommender Systems, DMKM: Applications
AAAI/2024/Proceedings/29441 - STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation.pdf

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[3]

The authors present the method with extensive (visual) descriptions. The authors provide a GitHub repository with the source code (https://github.com/LiangcaiSu/STEM). It has a read me with architecture description, data preparation steps, and execution of the method. However, the code does not have extensive comments explaining the steps of the methods, which makes interpretation rather difficult.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(3/3)

The authors use three datasets which are publicly available, with proper citations, and include statistics on each.  Direct links can be found in the implementation.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[2]

The authors clearly document the hyperparameters and the algorithm used to find the used configurations. The budget is not entirely clear for the grid search, but it indicates they exhaustively searched the grid.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[3]

The authors do not specifically state the procedure, but based on presented results one can perhaps deduce it is a one-shot result.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[3]

The method is complex, but the documentation provided in the paper is extensive. The cost could be lowered by improving the documentation in the implementation (Repository).
