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.
Stack for which part (model training, front-end, back-end or something else)? Anyway, i find Torch, TorchServe, PostgreSQL and NextJS are vital part of the project.
Also, for the ML part, why did you choose this model?
Do you mean GCE-GNN as smart recommender? If so, these are primary reasons (copied from SRec blog page),
User-free model, but utilize other user’s sequence during prediction. By user-free model, I mean the model doesn’t use user ID/representation on prediction. It’s a required feature for SRec recommender system since all visitor is anonymous.
Authors of GCE-GNN provide source code of the model. This saves some time to re-implement GCE-GNN or verify different GCE-GNN implementations (such as RecBole-GNN) by myself.
GCE-GNN model architecture/framework is relatively easy to understand.
What I dislike with NN is that they provide black box recommendations while users would like to understand WHY results are recommended
As user, sometimes i also feel this. That’s why i also create recommendation by game tags which show some explanation.
Stack for which part (model training, front-end, back-end or something else)? Anyway, i find Torch, TorchServe, PostgreSQL and NextJS are vital part of the project.
Do you mean GCE-GNN as smart recommender? If so, these are primary reasons (copied from SRec blog page),
As user, sometimes i also feel this. That’s why i also create recommendation by game tags which show some explanation.