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I have a 4 datasets with user features, item features, user-item rating and User-item link data. I'm trying to build a recommender system to recommend top 10 items to the user by maximizing NDCG as the metric.

So far I've used the user-item rating data with Implicit ALS using sparse matrix approach. I wanted to know how could the other data be trained and analyzed? Any good resources for similar problem?

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