Conversational Collaborative Filtering using External Data and MovieSent dataset

Code and data from the NAACL'21 short paper "You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions" by Volokhin et al.

MovieSent - dataset containing 489 movie-related conversations with fine-grained user sentiment labels about each mentioned movie. Conversations are in the MovieSent.json file.

Reviews were collected in April 2020. Initially a list of critics is compiled from more than 600 movies, their IDs are in films_rt_ids.json. Then for those critics all their reviews are scraped and put into reviews.tar.gz file.

To run the model:

1. Install requirements.txt
2. Run indexing.py to create an index of reviews based on the reviews.tsv.gz file.
3. Run sentiment_estimation.py to create a sentiment estimation model.
4. Run main.py for the final model. Training of CF model will occur at the same time, and can take a long time for a SVDpp model (KNN is much faster, ~20 seconds, if you just want to check if the code works).