I am trying to build a hybrid recommender system using lightFM that only recommends one of $3$ items. In my case, they are marketing campaigns that a company would like to recommend for users at a risk of churn. I have a bunch of useful user features and I can build a user item interaction matrix based on the users' feedback since I have access to historical data where in the past marketing campaigns were given. The feedback is computed based on whether the client stayed with the company or not after having received the campaign (binary response).
My question is, with very few items such as in my case, can item features be useful? For instance, say I collect a bunch of binary, categorical and numerical features for the items. I would have to one hot encode and normalize / binarize my features. However, if I have $10$ categories of one feature and only $3$ observations in my item dataset, I would end up with a quite short and quite sparse dataframe that contains little information (if not useless since I only have $3$ rows). I was wondering if there is any merit to feeding them as item features to my lightFM model in this case?