Search Results for author: Lixin Zhang

Found 6 papers, 2 papers with code

Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation

no code implementations28 Sep 2023 Chong Liu, Xiaoyang Liu, Lixin Zhang, Feng Xia, Leyu Lin

Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method.

Recommendation Systems Self-Supervised Learning

Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation

no code implementations15 Aug 2023 Chong Liu, Xiaoyang Liu, Ruobing Xie, Lixin Zhang, Feng Xia, Leyu Lin

A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels.

Recommendation Systems Retrieval

Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender

no code implementations29 May 2023 Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin

Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity.

Recommendation Systems Retrieval

UFNRec: Utilizing False Negative Samples for Sequential Recommendation

1 code implementation8 Aug 2022 Xiaoyang Liu, Chong Liu, Pinzheng Wang, Rongqin Zheng, Lixin Zhang, Leyu Lin, Zhijun Chen, Liangliang Fu

To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance.

Sequential Recommendation

Single-shot Embedding Dimension Search in Recommender System

no code implementations7 Apr 2022 Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, Hongzhi Yin

In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems.

Click-Through Rate Prediction Recommendation Systems

CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

2 code implementations13 Dec 2021 Chong Liu, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, Leyu Lin

State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations.

Click-Through Rate Prediction Contrastive Learning +2

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