Slide 27
Slide 27 text
前半戦でやったこと 2/2
● コンペごとの要約，コンペ全体の要約を作らせてみた．比較表も作ってみた．
プロンプト
"""Write a 300 letters summary
of the following kaggle
solution writeup:
"{text}"
300 LETTERS SUMMARY:"""
プロンプト
"""Create an overall summary of
the kaggle competition from the
following solution writeups:
"{text}"
OVERALL SUMMARY: """
プロンプト
"""Create an approach comparison
table by Markdown format from
the following kaggle solutions
(each row corresponds to each
solution):
"{text}"
APPROACH COMPARISON TABLE: """
Overall Summary
The Kaggle competition focused on improving the ranking of
items in a recommender system. The solutions varied in their
approaches, but they all shared common themes such as
feature engineering, ensemble methods, and incorporating
information from the session history. The winning solution used
a covisitation matrix to model relationships between features
and a neural network to make predictions.
2nd place solution
Team: SOS3 Leader: ONODERA Public Score: 0.60401 Private
Score: 0.60446
The candidate focused on improving features related to
item2item, including count, time difference, sequence
difference, weighted above features, and aggregation of these
features. They used XGBoost and CatBoost for model building
and then blended the results by rank. The candidate
acknowledged the contributions of cuDF and cuML and
expressed gratitude to RAPIDS for their assistance.