Search Results for author: Wang-Cheng Kang

Found 10 papers, 6 papers with code

Learning to Embed Categorical Features without Embedding Tables for Recommendation

no code implementations21 Oct 2020 Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi

Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.

Collaborative Filtering Natural Language Understanding +2

Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation

no code implementations12 Sep 2019 Wang-Cheng Kang, Julian McAuley

Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale.

Recommendation Systems Re-Ranking

Complete the Look: Scene-based Complementary Product Recommendation

1 code implementation CVPR 2019 Wang-Cheng Kang, Eric Kim, Jure Leskovec, Charles Rosenberg, Julian McAuley

We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.

Product Recommendation

Recommendation Through Mixtures of Heterogeneous Item Relationships

2 code implementations29 Aug 2018 Wang-Cheng Kang, Mengting Wan, Julian McAuley

Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data.

Knowledge Graph Embeddings Recommendation Systems

Self-Attentive Sequential Recommendation

5 code implementations20 Aug 2018 Wang-Cheng Kang, Julian McAuley

Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently.

Sequential Recommendation

Visually-Aware Fashion Recommendation and Design with Generative Image Models

no code implementations7 Nov 2017 Wang-Cheng Kang, Chen Fang, Zhaowen Wang, Julian McAuley

Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i. e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features.

Recommendation Systems

Translation-based Recommendation

1 code implementation8 Jul 2017 Ruining He, Wang-Cheng Kang, Julian McAuley

Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems.

Recommendation Systems Translation

Feature Learning based Deep Supervised Hashing with Pairwise Labels

1 code implementation12 Nov 2015 Wu-Jun Li, Sheng Wang, Wang-Cheng Kang

For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning.

Image Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.