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Linhong Zhu

Linhong Zhu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Social and Information NetworksInformation RetrievalArtificial IntelligenceComputation and Languagecs.CYDatabasesInformation TheoryMachine Learningmath.ITphysics.soc-ph

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Published work

7 published item(s)

preprint2026arXiv

A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems

Conventional recommendation systems frequently fail to fully exploit the high-dimensional semantic signals inherent in multimedia content, thereby limiting the fidelity of user preference modeling. While Multimodal Large Language Models (MM-LLMs) offer robust mechanisms for interpreting such complex data, their integration into latency-constrained, industrial-scale architectures remains a significant challenge. To address this, we propose a generalized framework for MM-LLM-driven multimedia understanding. Our methodology employs a tripartite architecture encompassing content interpretation, representation extraction, and systematic pipeline integration, instantiated via a LLaMA2-based model that generates descriptive captions subsequently ingested as tokenized categorical features. Empirical evaluation demonstrates the efficacy of this approach, yielding a $0.35\%$ increase in offline AUC and a $0.02\%$ improvement in online metrics at scale, substantiating the practical viability of leveraging MM-LLMs to enhance large-scale recommendation performance.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Attention Inequality in Social Media

Social media can be viewed as a social system where the currency is attention. People post content and interact with others to attract attention and gain new followers. In this paper, we examine the distribution of attention across a large sample of users of a popular social media site Twitter. Through empirical analysis of these data we conclude that attention is very unequally distributed: the top 20\% of Twitter users own more than 96\% of all followers, 93\% of the retweets, and 93\% of the mentions. We investigate the mechanisms that lead to attention inequality and find that it results from the "rich-get-richer" and "poor-get-poorer" dynamics of attention diffusion. Namely, users who are "rich" in attention, because they are often mentioned and retweeted, are more likely to gain new followers, while those who are "poor" in attention are likely to lose followers. We develop a phenomenological model that quantifies attention diffusion and network dynamics, and solve it to study how attention inequality grows over time in a dynamic environment of social media.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Latent Space Model for Road Networks to Predict Time-Varying Traffic

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learn the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly with given data. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks over competitors as well as a baseline graph-based LSM.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space and interactions are more likely to form between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space, with a quadratic convergence rate. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Temporal Learning and Sequence Modeling for a Job Recommender System

We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Partitioning Networks with Node Attributes by Compressing Information Flow

Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in content, with nodes having distinguishing features or attributes. In order to discover a network's modular structure, it is necessary to take into account not only its links but also node attributes. We describe an information-theoretic method that identifies modules by compressing descriptions of information flow on a network. Our formulation introduces node content into the description of information flow, which we then minimize to discover groups of nodes with similar attributes that also tend to trap the flow of information. The method has several advantages: it is conceptually simple and does not require ad-hoc parameters to specify the number of modules or to control the relative contribution of links and node attributes to network structure. We apply the proposed method to partition real-world networks with known community structure. We demonstrate that adding node attributes helps recover the underlying community structure in content-rich networks more effectively than using links alone. In addition, we show that our method is faster and more accurate than alternative state-of-the-art algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media

The growing popularity of social media (e.g, Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised \emph{tri-clustering} framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the proposed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California ballot Twitter data.

0 citations0 reviewsNo code linkedOpen access