DCT: A Deep Collaborative Filtering Approach Based on Content-Text Fused for Recommender Systems.
Zhi-Qiao ZhangJunhao WenJianing ZhouPublished in: CollaborateCom (1) (2020)
Keyphrases
- recommender systems
- collaborative filtering
- textual content
- matrix factorization
- user generated content
- user interests
- text content
- recommendation systems
- web personalization
- semantic content
- web documents
- user preferences
- text information
- content and structure
- data sparsity
- cold start problem
- user profiles
- personalized recommendation
- document content
- user feedback
- semantic information
- collaborative filtering algorithms
- information overload
- discrete cosine transform
- web images
- collaborative filtering recommender systems
- probabilistic matrix factorization
- user ratings
- information retrieval
- image compression
- textual information
- web content
- content features
- recommendation quality
- making recommendations
- netflix prize
- cold start
- user profiling
- information filtering
- item based collaborative filtering
- implicit feedback
- user modeling
- user similarity
- recommendation algorithms
- multimedia documents
- text mining
- metadata
- user generated
- movie recommendation
- latent factor models
- content based filtering
- prediction accuracy
- data fusion
- user context
- transfer learning
- product recommendation
- text documents
- multimedia content
- digital content