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I am working on an app recommendation problem. I don't have any app features, but I have user features. I've tried different similarity based models and also using a multiclass classification model's predicted probabilities to generate candidates. The later works simply because most users only use 1 app.

When I generate top 10 candidates, I get a hit rate of about 65% but the NDCG and other ranking metrics aren't great.

Once I generate candidates, is there a way to re-rank them?

I considered the XGBRanker but re-ranking on a massive dataset is hard. I have over 130 apps and over 70K users data right now. Pairwise ranking just blows up the dataset.

After generating candidates, do you re-train a model with the candidates for each user? That seems exhaustive again. This is largely a function of popularity bias in my dataset. 30 apps cover 80% of users.

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