Recommender Systems Course Project
Good-day everyone. I am an undergrad student and am currently taking a course on Recommender Systems. The course requires a mandatory project that can fall into one of the following four categories:
1. Innovative Improvement
-
Propose an improvement to an existing method.
-
Select a method from a top-tier conference paper (2023 or newer).
-
Implement both the original method and your proposed improvement.
-
Conduct experiments using all datasets from the original paper and compare the results.
-
The complexity of the original method may vary, and the improvement can be intricate or simple.
-
The scope of work, workload, and team size should be justified accordingly.
2. Comparative Analysis of Three or more Methods
-
Critically and empirically evaluate three or more well-known methods that solve the same problem.
-
Implement all three methods and perform a comparative experimental analysis.
-
Highlight key differences, strengths, and limitations of each method.
-
Provide well-supported observations and insights based on your findings.
3. Broad Comparative Evaluation
-
Evaluate five or more established methods for a given problem.
-
Implement these methods and demonstrate results across multiple datasets.
-
A thorough comparative analysis of performance will be appreciated.
4. Term Paper
- Select a topic and write a comprehensive term paper by reviewing and analyzing all major published results in the field.
I want to work on the 1st and specifically on Graph based models but I don’t know how to start. As for the knowlege to work on Graph ML I’ve been intrested in them so I learnt about them. I would like to someone to point me right direction and as the deadline is on 25th Feb I would like to finalize the project idea so that I can work on it.
Thank you for your time to read this and I would like to request to help me in this.
Thank you everyone.