SQL-Rank++: A Novel Listwise Approach for Collaborative Ranking with Implicit Feedback.
Zheng YuanDugang LiuWeike PanZhong MingPublished in: IJCNN (2022)
Keyphrases
- implicit feedback
- learning to rank
- ranking models
- web search
- relevance judgments
- user feedback
- ranking functions
- collaborative filtering
- query dependent
- ranking algorithm
- personalized ranking
- eye tracking
- loss function
- pairwise
- learning to rank algorithms
- clickthrough data
- explicit feedback
- user behavior
- search result
- relational databases
- information retrieval
- evaluation measures
- recommender systems
- search engine
- web search engines
- ranking svm
- database
- matrix factorization
- personalized recommendation
- databases
- search queries
- relevance feedback
- supervised learning
- benchmark datasets
- retrieval systems
- document retrieval
- retrieval effectiveness
- ranked list
- contextual information
- web pages
- relevant documents
- evaluation metrics
- user interaction
- language model
- information retrieval systems
- query logs
- semi supervised