Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Incorporating query-specific feedback into learning-to-rank models
Learning to Rank for Information Retrieval
A Theoretical Analysis of Pseudo-Relevance Feedback Models
Online Learning to Rank: Absolute vs. Relative
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
Image Search Reranking With Query-Dependent Click-Based Relevance Feedback
A feature selection method based on minimum redundancy maximum relevance for learning to rank
Improving retrieval performance by relevance feedback
Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback
A framework for query refinement with user feedback
Collaborative Filtering with Implicit Feedbacks by Discounting Positive Feedbacks
A relaxed ranking-based factor model for recommender system from implicit feedback
Adapting deep RankNet for personalized search
Position Bias Estimation for Unbiased Learning to Rank in Personal Search
Pairwise Preference Learning and Ranking
Learning to rank for robust question answering
A boosting approach to improving pseudo-relevance feedback
Learning to rank with click-through features in a reinforcement learning framework
A regression framework for learning ranking functions using relative relevance judgments
Adaptive relevance feedback in information retrieval
OHSUMED: an interactive retrieval evaluation and new large test collection for research
An Anti-Money Laundering Methodology: Financial Regulations, Information Security and Digital Forensics Working Together
Neutrality in evolutionary algorithms ... What do we know ?
brain tumor segmentation using k - means clustering and fuzzy c - means algorithms .
Ffsck: The Fast File-System Checker