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TABLETOP GAME
RECOMMENDATION
TABLETOP GAME
RECOMMENDATION
P
ortfolio
By Frida Putriassa
Course : Artificial Intelligence
FRIDA PUTRIASSA
T
h
i
s
P
ortfolio Belon
g
s
T
o
2110512104
C
C O
O N
N T
T E
E
BAGROUND
ABOUT DATASET
PREPROCESSING
USER TO USER COLLABORATIVE FILTERING
ITEM-BASED COLLABORATIVE FILTERING
CONCLUSION
N
N T
T
BAGROUND
BAGROUND
B
B II N
N G
G O
O
- - - - -
- - - - -
- - - -
- - - - -
- - - - -
-
B
B II N
N G
G O
O
- - - - -
- - - - -
- - - -
- - - - -
- - - - -
-
B
B II N
N G
G O
O
- - - - -
- - - - -
- - - -
- - - - -
- - - - -
-
WHY
A tabletop game recommendation system enhances gaming with personalized suggestions,
saving time, fostering community engagement, and uncovering hidden gems, contributing to a
vibrant gaming community.
ABOUT
DATASETS
ABOUT
DATASETS
KAGGLE DOCUMENTATION
FRIDA PUTRIASSA
This dataset Feature-rich multi-table dataset full of interesting information about Board Games
which can be used for tasks such as exploratory EDA, predictive modeling, or recommender systems.
This data set includes nine potential files for exploration and/or modeling :
GAMES
RATINGS_DISTRIBTION
THEMES
MECHANICS -
SUBCATEGORIES
ARTISTS_REDUCED
DESIGNERS_REDUCED
PUBLISHERS_REDUCED
USER_RATINGS
PREPROCESSING
PREPROCESSING
PREPROCESSING
PREPROCESSING
Import Library
Import Dataset
PREPROCESSING
PREPROCESSING
Cleaning Encoding
Encoding
PREPROCESSING
PREPROCESSING
Generate needed format (Columns contained Games and rows contained user)
USER TO USER
USER TO USER
COLLABORATIVE
FILTERING
COLLABORATIVE
FILTERING
START
NORMALIZATION
NORMALIZATION
SIMILARITY BETWEEN 2
USERS
SIMILARITY BETWEEN 2
USERS
SIMILARITY BETWEEN 2 USERS
SIMILARITY BETWEEN 2 USERS
SIMILARITY BETWEEN 2 USERS
SIMILARITY BETWEEN 2 USERS
GAME TO GAME
GAME TO GAME
ITEM-BASED
FILTERING
ITEM-BASED
FILTERING
START GAME
NORMALIZE
NORMALIZE
SIMILARITY BETWEEN 2 GAMES
SIMILARITY BETWEEN 2 GAMES
SIMILARITY MATRIX FOR A GAME
SIMILARITY MATRIX FOR A GAME
MOST SIMILAR GAMES
MOST SIMILAR GAMES
MOST SIMILAR GAMES
MOST SIMILAR GAMES
CONCLUSION
CONCLUSION
In conclusion, our exploration of an item-based collaborative filtering recommendation
system for tabletop games signifies a significant step forward in optimizing the gaming
experience. By strategically utilizing included user ratings, we've harnessed valuable
insights to develop a powerful recommender system, capable of offering tailored
suggestions based on both individual preferences and collective gaming behaviors.
THANK YOU
THANK YOU
FOR LISTENING
FOR LISTENING
See You Next Time!

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TABLETOP GAME RECOMMENDATION SYSTEM USING PYTHON