1 code implementation • 14 Nov 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li, Meng Wang
Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems.
1 code implementation • 14 Nov 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Yinfeng Li, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li
In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances.
1 code implementation • 8 Feb 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang song, Zhiheng Li, Depeng Jin, Yong Li
In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer.
1 code implementation • 18 Sep 2022 • GuanYu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin, Dong Li, Jianye Hao, Yong Li
Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions.
no code implementations • 1 Jan 2021 • Guan Ting Liu, Pu-Jen Cheng, GuanYu Lin
Representation learning on visualized input is an important yet challenging task for deep reinforcement learning (RL).