Julian McAuley

UC San Diego
https://cseweb.ucsd.edu/~jmcauley/
Publications147
h-index38
Citations10,233
Highly Influential Citations1,349
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  • Publications
  • Influence
Hidden factors and hidden topics: understanding rating dimensions with review text
TLDR
In order to recommend products to users we must ultimately predict how a user will respond to a new product. Expand
  • 1,144
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Image-Based Recommendations on Styles and Substitutes
TLDR
We are interested here in uncovering relationships between the appearances of pairs of objects, and particularly in modeling the human notion of which objects complement each other and which might be seen as acceptable alternatives. Expand
  • 1,000
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Self-Attentive Sequential Recommendation
TLDR
We propose a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions. Expand
  • 246
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VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
TLDR
We propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. Expand
  • 389
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Learning to Discover Social Circles in Ego Networks
TLDR
We propose an unsupervised method to learn which dimensions of profile similarity lead to densely linked circles that combines network structure and user profile information. Expand
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Community Detection in Networks with Node Attributes
TLDR
We develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. Expand
  • 617
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
TLDR
We build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. Expand
  • 814
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  • PDF
Inferring Networks of Substitutable and Complementary Products
TLDR
We build topic models that are trained to automatically discover topics from product reviews that are successful at predicting and explaining such relationships. Expand
  • 511
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  • PDF
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
TLDR
We model how tastes change due to the very act of consuming more products---in other words, as users become more experienced. Expand
  • 326
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  • PDF
Adversarial Audio Synthesis
TLDR
We introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. Expand
  • 193
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