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Hello! Would be cool if you guys @VincentSchuler and @fconti commented on your approach to the problem :D. I'm somewhat new to competitions and just would like to know how you went about it, just to learn a bit. As for me, I ended up using an LLM to concatenate the used rules (the text, not the numeric rules) by users and then predicting possible labels (text as well). I figured the rules made some semantic sense and that people ended up using similar rules most of the time with the devices they had already used. The end was a bit messy as I had to convert this text rule to the numeric one by deciding which devices to use. It worked fairly well, though I did manage to squeeze a much better score.
Anyways, had fun with it. Hope to hear from you guys. Cheers
Hello @Ilde !
Good game!
your solution seems very different from mine and also from Vincent's (which I reached on linkedin). it's nice that the top 3 have totally different approaches.
I am relatively new to the world of competitions, I developed a GNN based on the baseline paper published by wyze (https://openreview.net/forum?id=qynH28Y4xE).
Basically, I modeled the problem as a link prediction, in which a score is assigned to each possible link of each type. The architecture consists of a first embedding block for each node of the graph and then a head for calculating the probabilities for each of the links. So I selected the 50 best scores. I trained using positive and negative sampling from the training set.
The solution is quite similar to that of wyze although I re-implemented everything using pytorch geometric. I think the biggest differences:
- Training organized in batches;
- Positive and negative sampling different from the baseline;
- The mechanism for defining features on nodes and links.
In any case, I haven't had time to do hyper-parameter tuning, so I think the performance can improve a lot. I worked just with free google-colab.
I will definitely share the solution on github!
Oh cool, looking forward to it. Thanks for the write up. I think I used that just to toy with the sample submission, but was really looking for an excuse to learn more about the transformers library. Cheers
Hi @Ilde , this is my code! https://github.com/conti748/wyze-rule-recommendation
Oh cool! I'll check it out. Cheers