no code implementations • 21 Feb 2024 • Jianqiang Shen, Yuchin Juan, Shaobo Zhang, Ping Liu, Wen Pu, Sriram Vasudevan, Qingquan Song, Fedor Borisyuk, Kay Qianqi Shen, Haichao Wei, Yunxiang Ren, Yeou S. Chiou, Sicong Kuang, Yuan Yin, Ben Zheng, Muchen Wu, Shaghayegh Gharghabi, Xiaoqing Wang, Huichao Xue, Qi Guo, Daniel Hewlett, Luke Simon, Liangjie Hong, Wenjing Zhang
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking.
no code implementations • 14 Feb 2024 • Xinyuan Wang, Liang Wu, Liangjie Hong, Hao liu, Yanjie Fu
Additionally, we introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information in graph data.
1 code implementation • 2 Nov 2023 • Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model user/item collaborative and content semantics.
1 code implementation • 5 Jun 2023 • Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
But since sensitive features may also affect user interests in a fair manner (e. g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities.
no code implementations • 26 Aug 2022 • Qinyi Zhu, Liang Wu, Qi Guo, Liangjie Hong
Introducing a brand new workplace type naturally leads to the cold-start problem, which is particularly more common for less active job seekers.
no code implementations • 15 May 2019 • Andrew Stanton, Akhila Ananthram, Congzhe Su, Liangjie Hong
In this paper, we address how a company-aligned search experience can be provided with competing business metrics that E-commerce companies typically tackle.
no code implementations • 11 Dec 2018 • Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong
Industry-scale recommendation systems have become a cornerstone of the e-commerce shopping experience.
2 code implementations • 4 Nov 2017 • Kamelia Aryafar, Devin Guillory, Liangjie Hong
In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings.
no code implementations • 23 Jun 2017 • Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong
In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework.
no code implementations • 4 Jun 2017 • Ting Chen, Liangjie Hong, Yue Shi, Yizhou Sun
While latent factors of items can be learned effectively from user interaction data, in many cases, such data is not available, especially for newly emerged items.
no code implementations • 12 Apr 2016 • Liangjie Hong, Adnan Boz
In a real setting, users may be attracted by a subset of those items and interact with them, only leaving partial feedbacks to the system to learn in the next cycle, which leads to significant biases into systems and hence results in a situation where user engagement metrics cannot be improved over time.