28 citations · 68 across the 18 of their papers we have counts for
6 papers · 1 filter
Co-Factorization Model for Collaborative Filtering with Session-based Data
Binh Nguyen, Atsuhiro Takasu
Matrix factorization (MF) is a common method for collaborative filtering. MF represents user preferences and item attributes by latent factors. Despite that MF is a powerful method…
Boosting the Rating Prediction with Click Data and Textual Contents
ThaiBinh Nguyen, Atsuhiro Takasu
Matrix factorization (MF) is one of the most efficient methods for rating predictions. MF learns user and item representations by factorizing the user-item rating matrix. Further,…
Learning Representations from Product Titles for Modeling Shopping Transactions
Binh Nguyen, Atsuhiro Takasu
Shopping transaction analysis is important for understanding the shopping behaviors of customers. Existing models such as association rules are poor at modeling products that have…
NPE: Neural Personalized Embedding for Collaborative Filtering
ThaiBinh Nguyen, Atsuhiro Takasu
Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorl…
Collaborative Item Embedding Model for Implicit Feedback Data
ThaiBinh Nguyen, Kenro Aihara, Atsuhiro Takasu
Collaborative filtering is the most popular approach for recommender systems. One way to perform collaborative filtering is matrix factorization, which characterizes user preferenc…
A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data
ThaiBinh Nguyen, Atsuhiro Takasu
One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users…