| ---
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| license: apache-2.0
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| language:
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| - en
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| library_name: sentence-transformers
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| pipeline_tag: sentence-similarity
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| base_model: sentence-transformers/all-MiniLM-L6-v2
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| datasets:
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| - FronkonGames/steam-games-dataset
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| tags:
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| - transformers.js
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| - onnx
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| - sentence-transformers
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| - sentence-similarity
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| - feature-extraction
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| - recommendation
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| - video-games
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| - steam
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| ---
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|
|
| # gamespace.2
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|
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| gamespace.2 embeds video games into a 128-dimensional space where games that the same people enjoy
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| sit close together. It powers the [GameMerge](https://huggingface.co/spaces/GulkoA/gamemerge)
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| recommender. Output vectors are L2-normalised, so a dot product is the cosine similarity.
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| It is [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) fine-tuned on
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| Steam store data, like [gamespace.1](https://huggingface.co/GulkoA/gamespace.1). It also learns from
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| player behaviour: pairs of games that the same Steam reviewers engaged with are positive examples.
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| The model still reads only a game's store text, so it works for any game, including new ones with no
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| players yet.
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|
|
| ## Steam next-game benchmark
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|
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| The benchmark follows the protocol of SASRec (Kang & McAuley, ICDM 2018) on their Steam review crawl.
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| Each user's most recent reviewed game is held out and ranked against 100 randomly sampled games, and
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| also against the whole catalog. Text models score candidates by cosine similarity to the mean of the
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| user's earlier games. Games no longer on the Steam store have no text to embed, so they were removed
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| before the five-review filter. That leaves 250,955 users, 10,318 games and 3.05M interactions. The
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| paper had 334,730 users and 13,047 games. Popularity and SASRec were re-run on this exact split.
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| | Model | Learns from user histories | Hit@10, 100 negatives | NDCG@10, 100 negatives | Hit@10, all games | NDCG@10, all games |
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| |---|---|---|---|---|---|
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| | SASRec, reproduced | yes | 0.836 | 0.581 | 0.115 | 0.060 |
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| | Popularity | yes | 0.688 | 0.420 | 0.050 | 0.025 |
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| | **gamespace.2, soft-max over history** | **pairs from training histories** | **0.518** | **0.308** | **0.046** | **0.025** |
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| | gamespace.2, mean of history | pairs from training histories | 0.511 | 0.301 | 0.034 | 0.018 |
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| | gamespace.2 + CLIP header art | pairs from training histories | 0.520 | 0.309 | 0.037 | 0.019 |
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| | gamespace.1 | no | 0.438 | 0.251 | 0.027 | 0.014 |
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| | e5-large-v2 | no | 0.404 | 0.234 | 0.030 | 0.016 |
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| | Qwen3-Embedding-0.6B | no | 0.336 | 0.185 | 0.018 | 0.010 |
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| | bge-large-en-v1.5 | no | 0.334 | 0.191 | 0.026 | 0.014 |
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| | all-MiniLM-L6-v2 | no | 0.306 | 0.162 | 0.017 | 0.010 |
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| | CLIP header art only | no | 0.288 | 0.158 | 0.014 | 0.007 |
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| For reference, the SASRec paper reports 0.873 and 0.631 on its full 13,047-game set.
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| The soft-max row scores each candidate by a soft maximum (log-mean-exp, temperature 0.1, chosen on
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| the validation split) of its cosine similarity to each earlier game, instead of similarity to their
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| mean. Mixed histories are not pulled toward one average point, which matters most when ranking the
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| whole catalog. GameMerge uses it to combine your picks.
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|
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| gamespace.2 is the best of the text embedding models, ahead of much larger general-purpose embedders.
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| Sequential recommenders trained on the interaction data remain far stronger at predicting the next
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| game, because they also learn popularity and order. gamespace.2 trades that for working on any game
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| from its description alone.
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|
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| ## Training
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| - **Data.** The [Steam Games Dataset](https://huggingface.co/datasets/FronkonGames/steam-games-dataset)
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| has 147,352 apps. Soundtracks, demos, playtests, servers, SDKs, and empty records were removed,
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| leaving 138,920 games.
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| - **Objective.** Contrastive training uses `CachedMultipleNegativesRankingLoss` with 128 in-batch
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| negatives. There are three kinds of positive pairs:
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| - 70,000 pairs are two randomly degraded views of the same game.
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| - 70,000 pairs are games whose vote-weighted user-tag profiles have a cosine of at least 0.6.
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| - 66,904 pairs are co-liked games. There are 36,371 distinct pairs, each shown in both orders.
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| They are mined from the benchmark's training histories only, so no validation or test game is
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| used. A pair must be shared by at least 5 reviewers, it is scored by normalised PMI, and each
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| game keeps its 10 strongest partners.
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| - **Settings.** The projection is `Dense(384→128)`, initialised from PCA. Training runs one epoch at
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| learning rate 3e-5, with 128-token documents.
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|
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| A looser pair set, with at least 3 shared reviewers and 20 partners per game, scored lower on
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| validation, with 0.477 against 0.503 Hit@10. Pair quality matters more than quantity.
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|
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| ## Usage
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| The model reads one short document per game. Build it with `docs.py` from this repo, using the same
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| format as gamespace.1:
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|
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| ```
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| Hollow Knight. Genres: Action, Adventure, Indie. Tags: Metroidvania, Souls-like, ... Modes: Single-player. Developer: Team Cherry. Forge your own path...
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| ```
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|
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| ```python
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| from sentence_transformers import SentenceTransformer
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| model = SentenceTransformer("GulkoA/gamespace.2")
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| vec = model.encode("Hollow Knight. Genres: Action, Adventure, Indie. Tags: Metroidvania, Souls-like.")
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| ```
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|
|
| ### In the browser
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|
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| `onnx/model_quantized.onnx` is 22 MB, per-channel int8, with cosine 0.995 to the PyTorch model. It
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| runs with [transformers.js](https://huggingface.co/docs/transformers.js). Apply the 128x384 projection
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| in `dense.bin`, which holds float32 weights in row-major order followed by the 128 biases, to the
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| mean-pooled output:
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|
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| ```js
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| import { pipeline } from "@huggingface/transformers";
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|
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| const extractor = await pipeline("feature-extraction", "GulkoA/gamespace.2", { dtype: "q8" });
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| const w = new Float32Array(await (await fetch("https://huggingface.co/GulkoA/gamespace.2/resolve/main/dense.bin")).arrayBuffer());
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| const { data: x } = await extractor("cozy farming game with fishing", { pooling: "mean", normalize: false });
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| const y = Array.from({ length: 128 }, (_, i) => w[128 * 384 + i] + x.reduce((s, xj, j) => s + w[i * 384 + j] * xj, 0));
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| const norm = Math.hypot(...y);
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| const vec = y.map((v) => v / norm);
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| ```
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|
|
| ## Limitations
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|
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| - The behaviour pairs come from reviews written between 2010 and January 2018. Newer games benefit
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| only through what the model generalises from text.
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| - Steam display names identify reviewers, and different people can share a name.
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| - Similarity ignores popularity and quality, so obscure games rank alongside well-known ones.
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|
|
| ## License
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|
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| The model is licensed under Apache 2.0, the same as the base model. The store data is MIT-licensed.
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| The review crawl is from the UCSD Steam datasets (Kang & McAuley, 2018).
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|
|
