Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: Joint Deep Modeling of Users and Items Using Reviews for Recommendation
Recommender systems based on user reviews: the state of the art
Attributes coupling based matrix factorization for item recommendation
Collaborative filtering and deep learning based recommendation system for cold start items
Specializing Joint Representations for the task of Product Recommendation
Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews
A probabilistic rating inference framework for mining user preferences from reviews
Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
Coupled Item-based Matrix Factorization
Content-Boosted Collaborative Filtering for Improved Recommendations
WalkRanker: A Unified Pairwise Ranking Model with Multiple Relations for Item Recommendation
PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems
TriRank: Review-aware Explainable Recommendation by Modeling Aspects
Group Recommender Systems: Combining individual models
A hybrid of sequential rules and collaborative filtering for product recommendation
User-Based and Item-Based Collaborative Filtering Recommendation Algorithms Design
When do Recommender Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance
Efficient retrieval of recommendations in a matrix factorization framework
comparison of collaborative deep learning and nonnegative matrix factorization for recommender systems .
Deep Item-based Collaborative Filtering for Top-N Recommendation
Contextual Model-Based Collaborative Filtering for Recommender Systems
Green Vehicle Routing Problem with Time-Varying Tra ffic Congestion
Bracken: estimating species abundance in metagenomics data
HERBAL PLANTS USED IN TREATMENT OF RHEUMATOID ARTHRITIS : A REVIEW
A Comparative Study of Feature Extraction Algorithms in Customer Reviews
Similarity evaluation on tree-structured data