Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: Collectively Embedding Multi-Relational Data for Predicting User Preferences
A Three-Way Model for Collective Learning on Multi-Relational Data
Tensor Factorization for Multi-relational Learning
Interactive collaborative filtering
Collaborative Similarity Embedding for Recommender Systems
Modeling User Purchase Preference Based on Implicit Feedback*
Content Embedding Regularized Matrix Factorization for Recommender Systems
Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach
Recommendation by mining multiple user behaviors with group sparsity
Exploiting the web of data in model-based recommender systems
Implicit feedback for inferring user preference: a bibliography
Efficient bayesian hierarchical user modeling for recommendation system
Bayesian Probabilistic Matrix Factorization with Social Relations and Item Contents for recommendation
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
effective blending of two and three - way interactions for modeling multi - relational data .
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups
Link Prediction via Matrix Factorization
A Model of User Preference Learning for Content-Based Recommender Systems
Latent semantic models for collaborative filtering
Interaction-Rich Transfer Learning for Collaborative Filtering with Heterogeneous User Feedback
Incorporating Selectional Preferences in Multi-hop Relation Extraction
Introduction To The CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Veterinary Forensic Pathology, Volume 1
Influence of transmucosal designs of three one-piece implant systems on early tissue responses: a histometric study in beagle dogs.
A More General Robust Loss Function
Machine Learning: A Probabilistic Perspective