A comparitive study of recommendation quality using LightGCN.
Assess the performance of a LightGCN recommender model by editing embedding weights.
Perform a comparative study on the impact of different methods of aggregation weight calculation on the quality of recommendation.
Dataset Used - MovieLens [https://movielens.org/]
Final Result: Best Outcome is produced by using the neighbouring user and item counts.