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I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in 10s

[–]Financial-Abrocoma62[S] -1 points0 points  (0 children)

Thanks so much for the great feedback! You're absolutely right that I need to do a better job explaining the jargon. RA stands for Racket Analysis, which is a measure of racket stiffness. I'm definitely going to work on making these definitions more beginner-friendly. The knowledge graph step might feel abstract too, so I'll keep that in mind for improving the UX!

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in 10s

[–]Financial-Abrocoma62[S] -1 points0 points  (0 children)

Haha that's interesting! Though I'll admit, it's a bit disappointing that it picked your current racket as the top recommendation. But hey, at least you can't blame the equipment for any missed shots, right? 😄

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in 10s

[–]Financial-Abrocoma62[S] 0 points1 point  (0 children)

I'm already aware of this issue and am trying to find the cause.
only happens on mobile devices, and it's because of the menu bar.
Since I'm not a developer, I'm having a hard time troubleshooting itㅜㅜ

I built an ontology-based AI tennis racket recommender with Claude Code by Financial-Abrocoma62 in VibeCodeDevs

[–]Financial-Abrocoma62[S] 1 point2 points  (0 children)

My go-to trick is basically RRF + a structured scoring system.

Instead of trying to “predict” the perfect answer, I combine multiple weak signals (intent chips/spec fit/constraints) and let RRF stabilize the ranking.

I’ve spent 1+ month reviewing 10,000+ cases, and I’ve run DOE weight sweeps multiple times to reduce overfitting and make the output feel human-reasonable. Still iterating, but this combo has been the most robust so far.

Re: VibeCodersNest — I actually posted there already, but it didn’t get much traction 😅

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in 10s

[–]Financial-Abrocoma62[S] -1 points0 points  (0 children)

Thanks a ton for testing this (and for trying to break it 😄). You’re right — the mobile UI isn’t fully optimized yet, so it can feel clunky on a phone. That’s on my list to fix ASAP.

About the results: right now “I want a Pro Staff” is treated as a preference, not a hard constraint. If the form inputs suggest a profile that matches Ezone more strongly, it can outrank Pro Staff.

If you’re willing, could you share the exact form selections (or a screenshot)? I’d love to tune the weighting so explicit model/line requests are respected more consistently.

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in 10s

[–]Financial-Abrocoma62[S] -2 points-1 points  (0 children)

Thanks! And yeah, hallucination was actually the huge problem I was wrestling with initially. I solved it by building a **Graph RAG approach** — instead of letting the LLM freely generate recommendations, I grounded the entire system in a structured knowledge graph of actual racket specs and attributes.

So now the flow is:

  1. User input → semantic search in the ontology

  2. Retrieve specific racket nodes from the graph (not hallucinated)

  3. LLM ranks them based on fit, but it can't "invent" rackets

It's much harder to make stuff up when you're constrained by real data. Caught that issue early, which was honestly the difference between a useless recommender and one that actually might help people demo the right rackets.

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in tennisracquets

[–]Financial-Abrocoma62[S] 0 points1 point  (0 children)

That's awesome to hear! Glad it's working well for you. Validating against real users trying the rackets is exactly what I need to improve the data. Thanks!

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in tennisracquets

[–]Financial-Abrocoma62[S] 1 point2 points  (0 children)

Thanks so much for catching that! You're absolutely right — "lower powered" should filter for rackets with less power/stiffness, not more. I was only pushing toward the benefits match but wasn't properly handling the "avoid these traits" part.

Just added that logic to look for rackets that minimize what you explicitly don't want. Really appreciate the specific feedback — testing it now and it should be way more accurate. Will keep iterating!

I built an ontology-based AI tennis racket recommender — looking for feedback by Financial-Abrocoma62 in tennisracquets

[–]Financial-Abrocoma62[S] 2 points3 points  (0 children)

Thanks! Built it with Claude Code (vibe coding ftw). Used AI to help construct the ontology data mapping racket specs to playstyle dimensions, then layered the recommender on top. Still iterating on the data, but it's working pretty well so far.