Chanyoung Park

Measuring the eccentricity of items

2017

The long-tail phenomenon tells us that there are many items in the tail. However, not all tail items are the same. Each item acquires different kinds of users. Some items are loved by the general …

Beyond Learning from Next Item

2022 · Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized interest drift based on each …

Convolutional Matrix Factorization for Document Context-Aware Recommendation

2016

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to …

Collaborative Translational Metric Learning

2018

Recently, matrix factorization-based recommendation methods have been criticized for the problem raised by the triangle inequality violation. Although several metric learning-based approaches have been proposed to overcome this issue, existing approaches typically project each user …