no code implementations • 28 Jan 2023 • Junsu Cho, Dongmin Hyun, Dong won Lim, Hyeon jae Cheon, Hyoung-iel Park, Hwanjo Yu
To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics.
1 code implementation • 14 Sep 2022 • Dongmin Hyun, Chanyoung Park, Junsu Cho, Hwanjo Yu
We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history.
Ranked #1 on Sequential Recommendation on Amazon Cell Phones
1 code implementation • 8 Jul 2021 • Junsu Cho, SeongKu Kang, Dongmin Hyun, Hwanjo Yu
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i. e., session).
1 code implementation • 29 Apr 2021 • Junsu Cho, Dongmin Hyun, SeongKu Kang, Hwanjo Yu
Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items.
1 code implementation • COLING 2020 • Dongmin Hyun, Junsu Cho, Hwanjo Yu
We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain.
1 code implementation • Conference 2020 • Dongmin Hyun, Junsu Cho, Chanyoung Park, Hwanjo Yu
More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future.
no code implementations • 7 Sep 2020 • Beomjo Shin, Junsu Cho, Hwanjo Yu, Seungjin Choi
Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag.