4 citations · 6 across the 5 of their papers we have counts for
6 papers
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…
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…
Recommending Short-lived Dynamic Packages for Golf Booking Services
Robin Swezey, Young-joo Chung
We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items…
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization
Ramin Raziperchikolaei, Tianyu Li, Young-joo Chung
Autoencoder-based hybrid recommender systems have become popular recently because of their ability to learn user and item representations by reconstructing various information sour…