| --- |
| title: SurfMatch |
| emoji: 🏄 |
| colorFrom: blue |
| colorTo: red |
| sdk: gradio |
| sdk_version: '5.9.1' |
| app_file: app.py |
| pinned: false |
| --- |
| |
| # SurfMatch |
|
|
| **Dataset:** Yoad22/surfmatch-dataset - 15,000 synthetic rider profiles, 150-board catalog |
| **Embedding model:** sentence-transformers/all-mpnet-base-v2 - 768-dimensional embeddings |
| **Generation model:** Qwen/Qwen3-1.7B - structured custom board spec + rationale |
| **Space:** https://huggingface.co/spaces/Yoad22/surfmatch |
|
|
| --- |
|
|
| ## What This Is |
|
|
| A surfboard recommendation app. Tell it your height, weight, skill level, current board, |
| typical conditions, and what you're trying to achieve, and it returns the 3 closest-matching |
| boards from a 150-board catalog, plus a 4th board that a small language model designs |
| specifically for you, including dimensions, tail, fins, construction, and a written rationale. |
|
|
| The app is built to also serve surfers who want to *change* styles, not just riders shopping |
| within the style they already have. |
|
|
| --- |
|
|
| ## How It Works |
|
|
| **1. Category** is decided from the rider's stated goal and skill level using a fixed rule, |
| not from text similarity. Testing embeddings for this decision directly (see Key Results) |
| showed they aren't reliable enough for it on their own. |
|
|
| **2. Physical fit** combines two real sizing guidelines: heavier and less experienced riders |
| need more volume, and taller and less experienced riders want relatively more length, each |
| measured as a percentile within the board's own category (categories differ hugely in scale, |
| so raw liters or feet aren't comparable across them). |
|
|
| **3. Embedding similarity** (all-mpnet-base-v2) ranks boards within the already-correct |
| category by how well a rider's profile matches each board's description - the comparison the |
| model is demonstrably good at. |
|
|
| **4. Diversity** picks the final 3 boards so they differ from each other on tail, fins, or |
| construction, instead of returning 3 near-identical boards. |
|
|
| **5. The 4th board** is generated by Qwen3-1.7B: given the rider's percentiles and the |
| category's real dimension ranges, the model is asked to reason about the right sizing in |
| words first, then commit to dimensions, tail, fins, construction, and a rationale, as |
| structured JSON. It's explicitly allowed to make unconventional choices (e.g. quad fins on a |
| longboard) that the fixed 150-board catalog doesn't offer. |
|
|
| --- |
|
|
| ## Key Results |
|
|
| | Metric | Value | |
| |---|---| |
| | Boards in catalog | 150 | |
| | Rider profiles generated | 15,000 | |
| | Embedding model hit_rate@1 (vs. 20% random baseline) | 41.6% | |
| | Embedding model hit_rate@3 (vs. 49% random baseline) | 45.0% | |
| | Silhouette score by true category (PCA-50 space) | 0.053 | |
| | Diversity: hit real per-category ceiling on tail | 100.0% | |
| | Diversity: hit real per-category ceiling on fins | 97.3% | |
| | Diversity: hit real per-category ceiling on construction | 96.3% | |
| | Diversity: hit the ceiling on all three at once | 93.7% | |
| | Custom board JSON parse rate | ~90% | |
|
|
| The embedding model captures real signal about a rider's best-fit category (41.6% top-1 |
| accuracy against a 20% random baseline), but that signal is not clean enough to decide the |
| category on its own - the silhouette score of 0.053 confirms categories overlap heavily in |
| embedding space. That's why category is decided by a fixed rule and embeddings are used only |
| to rank within a category, where they change at least one of the three recommended boards |
| for roughly half of all riders tested - real, independent work, not a no-op. |
|
|
| --- |
|
|
| ## An Honest Limitation |
|
|
| The custom board generator's written reasoning (`sizing_reasoning`) explicitly and correctly |
| describes how a rider's weight and height percentiles should affect the board's dimensions in |
| nearly every case. But across three separate real experiments at 30-rider scale - raw |
| numbers, percentile-grounded numbers, and percentile-grounded numbers with forced |
| reasoning-before-numbers - the model's actual output dimensions barely correlated with |
| weight or height (correlation near 0 each time), while skill-based sizing worked reliably |
| and correctly (correlation consistently and correctly signed). In other words: this small |
| model's stated reasoning is not fully faithful to what actually drives its numeric output. |
| This is a genuine, tested limitation of a 1.7B-parameter model on this task, documented here |
| rather than hidden - the skill-based sizing and the category/tail/fins/construction choices |
| all work as intended. |
|
|
| --- |
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|
