I’m looking to match two pieces of text - e.g. IMDb movie descriptions and each person’s description of the type of movies they like. I have an existing set of ~5000 matches between the two. I particularly want to overcome the cold-start problem: what movies to recommend to a new user? When a new movie comes out, to which users should it be recommended? I see two options:
- Run each description of a person through an LSTM; do the same for each movie description; concatenate the results for some subset of possible combinations of people and movies, and attach to a dense net to then predict whether it’s a match or not
- Attempt to augment collaborative filtering with the output from running the movie description and person description through a text learner.
Are these tractable approaches?