23_Mini_Project : ✧ Book-Recommendation ✧

Backgrounds

We initiated this project to deepen our understanding of the ongoing advancements and emerging trends in the field of recommendation systems.
We chose LightGCN and GLocal-K to develop models that precisely capture users’ inherent preferences while ensuring scalability and efficiency.
As research increasingly explores integrating recommendation systems with large language models (LLMs), we plan to maintain an active interest and continue our exploration in this direction.

This is a large-scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, and it includes rich features such as:
- User Reviews (ratings, text, helpfulness votes, etc.)
- Item Metadata (descriptions, price, raw image, etc.)
- Links (user-item / bought together graphs)
Link : https://amazon-reviews-2023.github.io

Taking into account that the actual rating matrix is sparse and side information is limited, we aimed to implement an accurate recommendation model tailored to each user’s preferences.
To achieve this, we formed teams of two, studied the relevant research papers, and implemented the two models described below.

Paper : https://arxiv.org/abs/2108.12184
Reference : https://paperswithcode.com/paper/glocal-k-global-and-local-kernels-for

After performing EDA on the Amazon book dataset, we found that there were relatively few user reviews per individual item, despite the dataset containing a wide variety of different items.
Since this was our first experience implementing a research paper, we encountered many difficulties, and it’s regrettable that we weren’t able to modify the model itself to better suit our dataset.
We also regret not being able to implement an app or web interface to showcase the recommended results, which highlights a technical shortcoming in our approach.