This repo contains the code and the data used in the Recommender Systems 2022 Challenge @ Politecnico di Milano.
The application domain is TV shows recommendation. The datasets we provide contains both interactions of users with TV shows, as well as features related to the TV shows. The main goal of the competition is to discover which previously unseen items (TV shows) a user will interact with. Each TV show (for instance, "The Big Bang Theory") can be composed by several episodes (for intance, episode 5, season 3) but the data does not contain the specific episode, only the TV show. If a user has seen 5 episodes of a TV show, there will be 5 interactions with that TV show. The goal of the recommender system is not to recommend a specific episode, but to recommend a TV show the user has not yet interacted with.
The datasets includes around 1.8M interactions, 41k users, 27k items (TV shows) and two features: the TV shows length (number of episodes or movies) and 4 categories. For some user interactions the data also includes the impressions, representing which items were available on the screen when the user clicked on that TV shows. The training-test split is done via random holdout, 85% training, 15% test. The goal is to recommend a list of 10 potentially relevant items for each user.
Note that Recommenders come from this repository.
The best model for my best submission is a hybrid model consisting of various models with distinct hyperparameters, each one individually trained. The models are trained on a stacked URM-ICM matrix, using both information regarding users and regarding features.
Specifically, the best model was built using the following models:
- SLIM ElasticNet
- RP3-Beta
- EASE-R
The model building process was incremental, with the SLIM ElasticNet model initially outperforming the others. However, by combining the results of each model with a weighted sum of the scores, I was able to achieve increasingly better outcomes.
The evaluation metric was MAP@10.
- Private Leaderboard Final Position: 14/93 (score: 0.05957)
- Public Leaderboard Final Position: 17/93 (score: 0.06005)
- Programming Language:
- Libraries:
- Prof. Paolo Cremonesi
- Prof. Maurizio Ferrari Dacrema