Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Online Learning to Rank with Top-k Feedback
Learning to rank: from pairwise approach to listwise approach
Continuation Methods and Curriculum Learning for Learning to Rank
Adaptive relevance feedback in information retrieval
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm
Fast als-based matrix factorization for explicit and implicit feedback datasets
Document selection methodologies for efficient and effective learning-to-rank
Letor: Benchmark dataset for research on learning to rank for information retrieval
FA*IR: A Fair Top-k Ranking Algorithm
A hybrid approach for recommendation system with added feedback component
LiMe: linear methods for pseudo-relevance feedback
Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks
NeuRec: On Nonlinear Transformation for Personalized Ranking
Alternating least squares for personalized ranking
A unified optimization framework for robust pseudo-relevance feedback algorithms
Adapting deep RankNet for personalized search
Image Search Reranking With Query-Dependent Click-Based Relevance Feedback
A boosting approach to improving pseudo-relevance feedback
Learning the weight of the query term from the relevance feedback
Learning to rank relational objects and its application to web search
Enterprise content management research: a comprehensive review
Semantic Feature Augmentation in Few-shot Learning.
libdai : a free and open source c + + library for discrete approximate inference in graphical models .
Low-dose oral isotretinoin for moderate to severe seborrhea and seborrheic dermatitis: a randomized comparative trial
Adapting ranking SVM to document retrieval