SRec.ai – ML-based Steam video game recommender

Hacker News

Hi everyone, In the past few months, I've been working to build ML-based recommender systems for Steam video games. I built this since I'm not satisfied with recommendations shown by Steam, especially for non-popular games. SRec provides 3 kinds of recommendation systems, * Smart recommender. It's based on GCE-GNN (Global Context Enhanced Graph Neural Networks) model with some modifications. * 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. It's strongly recommended to use either smart recommender or recommendation by game tags. Currently, smart recommender has poor performance when recommending extremely popular games. The performance of recommendation by similar game tags is limited by how Steam user apply tags to each games. Some technical details of Smart recommender can be seen on https://srec.ai/blog/srec-gcegnn . On each game page, SRec shows various game details including Steam Deck compatibility, content warning and all game tags. SRec also shows review insight for most games that shows most frequently mentioned keywords where you can read some top reviews which mention selected keyword. While you can access SRec on either desktop or mobile device, I recommend you to access SRec with desktop since I have no experience building front-end. But feel free to ask any question or leave feedback.

Visit product View on Hacker NewsNov 18, 2023

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, context · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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