Personalized Game Recommendation system for Steam

Recommendation engine for Steam games based on data collected from Steam Web API. Please see the attached report for complete details of the project

Data

Userid-playtime data is collected from the Steam Web API. Since Steam does not maintain explicit ratings for games, we derive our ratings based on playtime. The data can be found at - https://drive.google.com/drive/folders/1ESRLG9heVA5K4e0wYchkdi8KpqKOmh_b?usp=sharing

Models

Two classes of models were developed:

• Binary Ratings (Recommendations based on whether user bought the game or not)
• Continuous Ratings (Recommendations based on continuous ratings on the [0,1] scale)

Binary Ratings Models

Pleaes refer to the notebooks CollaborativeFiltering.ipynb and Implicit Matrix Factorization.ipynb for Binary Ratings based models.

Continous Ratings Models

Pleaes refer to the notebook ContinousRatingSurprise.ipynb for continous model. This has been implemented using the surprise library.

Results

Binary Models

Model	Precision@10	Recall@10
Baseline 1	0.0924	0.0352
Baseline 2	0.1086	0.0468
Pearson Correlation	0.4625	0.2057
Cosine Similarity	0.4746	0.2089
Implicit Linear	0.5012	0.2287
Implicit Log	0.5580	0.2559

Continuous Models

Model	RMSE
Baseline	0.2871
Cosine Similarity	0.2660
Matrix Factorization	0.2526