Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features.
Alexander KrauckDavid PenzMarkus SchedlPublished in: RecSys Challenge (2021)
Keyphrases
- lightweight
- recommender systems
- collaborative filtering
- user preferences
- matrix factorization
- recommendation systems
- information overload
- user profiles
- cold start problem
- cold start
- user modeling
- recommendation algorithms
- user profiling
- content based filtering
- collaborative filtering recommender systems
- recommendation quality
- trust aware
- compound critiques
- data sparsity
- wireless sensor networks
- user model
- information filtering
- dos attacks
- item recommendation
- online dating
- user interests
- active user
- collaborative recommendation
- development environments
- personal preferences
- product recommendation
- personalized recommendation
- user ratings
- user feedback
- item based collaborative filtering
- authentication protocol
- low cost