The article is updated.

Hey thank you Liad 🙂

Preslav as usual such a deep and detailed feedback. Thank you so much 🙂
Directly to your points.
1) Good to know that there is BG. Will use it.
2) Mean Reciprocal Rank – I would prefer MAP, MRR is used to when you have ranking in a list measures the first correct item, so normally is used if you want to predict only one, which is next and use ranking to see where is in the list, if it is 2nd or 3d it makes the magic which is in the formula. I will update the article.
3. I need clusters – because I want to have user interests representations somehow, which can be easier if it is in clusters, especially for sites with more than 50K articles.
4. RNN to predict next cluster of interest, not article they have 2-3 days life, and because it is sequence data. Our patterns as humans are changing so for me clusters are “topic of interests” somehow.
5. If I upload the updated file probably you will see more.
6. Only for RNN is 58% you will see it on new file but not whole logic is implemented with the three metrics mentioned below. I will need like 2-3 hours more 🙂
7. Two of them are – MRR and MAP for sure the other one is made by me 🙂 Here is some info https://stats.stackexchange.com/questions/127041/mean-average-precision-vs-mean-reciprocal-rank
8. It is supposed to be. Mentioned into the diagram. not in code

I think there were some in the code. Around 58% for RNN after 20 epoch.
Wow we uploaded wrong file. I will update it later today. Thanks

I like all the presentations, but the most cool part was the party. Great fun and great atmosphere 🙂