Improving the Discoverability of Indie Games by Leveraging their Similarity to Top-Selling Games
Bibliographic record
Abstract
Indie games often lack visibility as compared to top-selling games due to their limited marketing budget and the fact that there are a large number of indie games. Players of top-selling games usually like certain types of games or certain game elements such as theme, gameplay, storyline. Therefore, indie games could leverage their shared game elements with top-selling games to get discovered. In this paper, we propose an approach to improve the discoverability of indie games by recommending similar indie games to gamers of top-selling games. We first matched 2,830 itch.io indie games to 326 top-selling Steam games. We then contacted the indie game developers for evaluation feedback and suggestions. We found that the majority of them (67.9%) who offered verbose responses show positive support for our approach. We also analyzed the reasons for bad recommendations and the suggestions by indie game developers to lay out the important requirements for such a recommendation system. The most important ones are: a standardized and extensive tag and genre ontology system is needed to bridge the two platforms, the expectations of players of top-selling games should be managed to avoid disappointment, a player’s preferences should be integrated when making recommendations, a standardized age restriction rule is needed, and finally, the recommendation tool should also show indie games that are the least similar or less popular.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".