Discover your next favorite game. SRec helps you find similar Steam games with what you already like. We provide 3 kinds of recommendations which show different variety of similar games.
I built SRec since I’m not satisfied with the recommendations shown by Steam, especially for indie/unpopular games. There are 3 available recommendation types,
Smart recommender. It’s based on GCE-GNN (Global Context Enhanced Graph Neural Networks) model with some modifications. Addition details can be seen SRec blog post.
Recommendation by similar game tags. This recommender provide explanation by showing top-5 most similar tags between chosen and recommended games.
Recommendation by Steam user preferences.
Currently, smart recommender has poor performance when recommending extremely popular games. The performance of recommendation by similar game tags is limited by how Steam users apply tags to each games. On each game page, SRec also provide review insight that shows most frequently mentioned keywords where you can read some top reviews which mention selected keyword.
I use Torch to train the model and use TorchServe to deploy the model. I only use an RTX 3060 for this project. Feel free to ask any questions.
P.S. I cannot include any link on this comment since it got hit by spam filter.
Hi r/MachineLearning,
I built SRec since I’m not satisfied with the recommendations shown by Steam, especially for indie/unpopular games. There are 3 available recommendation types,
Currently, smart recommender has poor performance when recommending extremely popular games. The performance of recommendation by similar game tags is limited by how Steam users apply tags to each games. On each game page, SRec also provide review insight that shows most frequently mentioned keywords where you can read some top reviews which mention selected keyword.
I use Torch to train the model and use TorchServe to deploy the model. I only use an RTX 3060 for this project. Feel free to ask any questions.
P.S. I cannot include any link on this comment since it got hit by spam filter.