Search Results for author: Nian Li

Found 8 papers, 1 papers with code

Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation

no code implementations7 Mar 2024 Nicholas Sukiennik, Chen Gao, Nian Li

We formalize our definition of a "deep" filter bubble within this context, and then explore various correlations within the data: first understanding the evolution of the deep filter bubble over time, and later revealing some of the factors that give rise to this phenomenon, such as specific categories, user demographics, and feedback type.

Recommendation Systems

Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network

no code implementations18 Feb 2024 Lin Chen, Fengli Xu, Nian Li, Zhenyu Han, Meng Wang, Yong Li, Pan Hui

We propose a novel REasoning meta-STRUCTure search (ReStruct) framework that integrates LLM reasoning into the evolutionary procedure.

Language Modelling Large Language Model +1

Large Language Model-Empowered Agents for Simulating Macroeconomic Activities

no code implementations16 Oct 2023 Nian Li, Chen Gao, Yong Li, Qingmin Liao

In this work, we take an early step in introducing a novel approach that leverages LLMs in macroeconomic simulation.

Decision Making Language Modelling +2

Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System

no code implementations25 Aug 2023 Yunzhu Pan, Nian Li, Chen Gao, Jianxin Chang, Yanan Niu, Yang song, Depeng Jin, Yong Li

Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model.

Recommendation Systems

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks

1 code implementation5 Nov 2021 Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, Yong Li

With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.

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