Search Results for author: McAuley Julian

Found 5 papers, 2 papers with code

Learning Consumer and Producer Embeddings for User-Generated Content Recommendation

1 code implementation25 Sep 2018 Kang Wang-Cheng, McAuley Julian

User-Generated Content (UGC) is at the core of web applications where users can both produce and consume content.

Collaborative Filtering

SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation

no code implementations16 Jun 2017 Cai Chenwei, He Ruining, McAuley Julian

Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems.

Sequential Recommendation

Deduplication in a massive clinical note dataset

no code implementations19 Apr 2017 Shenoy Sanjeev, Kuo Tsung-Ting, Gabriel Rodney, McAuley Julian, Hsu Chun-Nan

In clinical notes data, duplication (and near duplication) can arise for many reasons, such as the pervasive use of templates, copy-pasting, or notes being generated by automated procedures.

Clustering

Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items

no code implementations31 Mar 2016 He Ruining, Lin Chunbin, McAuley Julian

To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i. e., substitutes), to generating stylish outfits that are visually consistent, among other applications.

Inferring Networks of Substitutable and Complementary Products

2 code implementations29 Jun 2015 McAuley Julian, Pandey Rahul, Leskovec Jure

These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other.

Link Prediction Recommendation Systems

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