How to use LightFM with sparse interaction data?

I am currently exploring recommender systems for a masters project at university. My dataset consists of tweets by users with a bunch of brief user metadata such as location and item metadata such as tweet body, hashtags used and industry, sector information about hashtag if present, timestamp etc.

My problem is I'm finding it difficult to recommend items because there is only ever a one-to-one interaction between a user and their authored post. The item features slightly increase scores on the training set but I have no luck on the test set.

Am I maybe looking at a different approach? I do have a slight profile for each user on the basis of their post content but how would I model that for recommendation? Does the sparsity of the interaction matrix matter? I'm quite new to all this so most of my issues are from how to frame my input data.

Start by reviewing the issue's questions about one-to-one interactions, sparse matrices, user profiles, and item features against LightFM's documented input model. Done would be a clear, project-specific explanation of whether this data framing is supported and what approach the user should investigate.