Search Results for author: Liangjie Hong

Found 11 papers, 3 papers with code

LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

no code implementations14 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.

Collaborative Large Language Model for Recommender Systems

1 code implementation2 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.

Hallucination Language Modelling +2

Path-Specific Counterfactual Fairness for Recommender Systems

1 code implementation5 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.

Blocking counterfactual +4

Remote Work Optimization with Robust Multi-channel Graph Neural Networks

no code implementations26 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.

Vocal Bursts Type Prediction

Revenue, Relevance, Arbitrage and More: Joint Optimization Framework for Search Experiences in Two-Sided Marketplaces

no code implementations15 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.

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

2 code implementations4 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.

Click-Through Rate Prediction Ensemble Learning

On Sampling Strategies for Neural Network-based Collaborative Filtering

no code implementations23 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.

Collaborative Filtering

Joint Text Embedding for Personalized Content-based Recommendation

no code implementations4 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.

News Recommendation Recommendation Systems

An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization

no code implementations12 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.

Recommendation Systems Thompson Sampling

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