A New Users Rating-Trend Based Collaborative Denoising Auto-Encoder for Top-N Recommender Systems.
Zeshan Aslam KhanSyed ZubairKashif ImranRehan AhmadSharjeel Abid ButtNaveed Ishtiaq ChaudharyPublished in: IEEE Access (2019)
Keyphrases
- recommender systems
- denoising
- collaborative filtering
- recommendation quality
- information overload
- active user
- collaborative filtering recommender systems
- cold start problem
- user profiles
- user ratings
- matrix factorization
- user modeling
- user preferences
- rating prediction
- cold start
- image denoising
- user interests
- personal preferences
- user profiling
- user model
- user feedback
- user modelling
- personalized recommendation
- collaborative recommender systems
- trust aware
- video coding
- recommendation systems
- collaborative filtering algorithms
- image processing
- injection attacks
- total variation
- natural images
- recommendation algorithms
- user context
- bit rate
- content based filtering
- data sparsity
- multi user
- motion estimation
- user generated content
- online dating
- user centric
- netflix prize
- implicit feedback
- multiple users
- compound critiques
- video codec