Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: Optimizing Factorization Machines for Top-N Context-Aware Recommendations
Factorization Machines with libFM
Parallel matrix factorization for recommender systems
Mining contextual movie similarity with matrix factorization for context-aware recommendation
Collaborative Filtering Recommendation Based on All-Weighted Matrix Factorization and Fast Optimization
Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback
Sparse linear methods with side information for top-n recommendations
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
Matrix factorization+ for movie recommendation
Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
Recurrent Poisson Factorization for Temporal Recommendation
Query-driven context aware recommendation
Hierarchical collaborative embedding for context-aware recommendations
Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation
Pseudo-Relevance Feedback Based on Matrix Factorization
Biclustering neighborhood-based collaborative filtering method for top-n recommender systems
A hybrid approach of topic model and matrix factorization based on two-step recommendation framework
Explaining Recommendations Using Contexts
Fast maximum margin matrix factorization for collaborative prediction
Knowledge Factorization
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Networks and epidemic models
Running head : Ternary Eye Movement Classification Automated Classification and Scoring of Smooth Pursuit Eye Movements in Presence of Fixations and Saccades
Reply to Commentary by Trentham et al. on "Caged Phosphate and the Slips and Misses in Determination of Quantum Yields for Ultraviolet-A-Induced Photouncaging" by Gasser et al.
Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks