4 citations · 12 across the 9 of their papers we have counts for
6 papers · 1 filter
Meta-Shop: Improving Item Advertisement For Small Businesses
Yang Shi, Guannan Liang, Young-joo Chung
In this paper, we study item advertisements for small businesses. This application recommends prospective customers to specific items requested by businesses. From analysis, we fou…
One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations
Ramin Raziperchikolaei, Young-joo Chung
In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related user-item pairs among a la…
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation
Shion Ishikawa, Young-joo Chung, Yu Hirate
Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and e…
Learning Similarity Preserving Binary Codes for Recommender Systems
Yang Shi, Young-joo Chung
Hashing-based Recommender Systems (RSs) are widely studied to provide scalable services. The existing methods for the systems combine three modules to achieve efficiency: feature e…
Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
Ramin Raziperchikolaei, Young-joo Chung
Neural network-based collaborative filtering systems focus on designing network architectures to learn better representations while fixing the input to the user/item interaction ve…
Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization
Yang Shi, Young-joo Chung
Cross-modal retrieval aims to search for data with similar semantic meanings across different content modalities. However, cross-modal retrieval requires huge amounts of storage an…