Currently, I am interested in building a recommendation system. I want to build it as a learning to rank problem using either
I am reading two papers about the process:
For defining the labels I plan to use an implicit score similar to the approach in the first linked paper. For each user I have access to information about whether the user purchased, liked the item, clicked on the item and also if they removed the item from the cart.
My questions are:
Through all of the research I have done so far the goal of the recommender system is to provide users a list of NEW content that they might be interested in buying. When training with the data I mentioned above the user has implicitly expressed interest in each item. Using the items from this list that were not purchased are not necessarily new content. I am wondering if any item the user interacted with that was not purchased or liked but interacted with should be considered as a candidate for recommendation? Has anyone actually recommended a product that a user has purchased before?
If indeed I am to consider only new content in the recommendation phase then am I supposed to predict a relevance score on ALL
(user, item)pairs that have not yet had an interaction? What if I have over 10k products? This seems like it will not scale very well.
Any comments, insights or feedback would be greatly appreciated!