Learning binary codes for collaborative filtering

By Ke Zhou, Hongyuan Zha. 2012

111 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Representation & Self-Supervised Learning, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Xiangnan He
2. Tat-seng Chua
3. Xiang Wang
4. Xing Xie
5. Fuzheng Zhang
6. Hanwang Zhang
7. Yongfeng Zhang
8. Wei David Liu

In Marketing

1. Xing Xie
2. Tat-seng Chua
3. Fumin Shen
4. Defu Lian
5. Ke Wang
6. Fuzheng Zhang
7. Huanbo Luan
8. Jiaxi Tang

In Representation & Self-Supervised Learning

1. Xiangnan He
2. Tat-seng Chua
3. Xing Xie
4. Jiaxi Tang
5. Ke Wang
6. Hanwang Zhang
7. Fumin Shen
8. Sanjiv Kumar

In Recommender Systems

1. Xiangnan He
2. Tat-seng Chua
3. Xing Xie
4. Fuzheng Zhang
5. Jiaxi Tang
6. Ke Wang
7. Xiang Wang
8. Wei-Ying Ma

Citations in the corpus

• Collaborative Deep Learning for Recommender Systems (2014, neutral): “Similar to [41], in order to be consistent with the implicit feedback setting of the first two datasets, we extract only positive ratings (rating 5) for training and testing.”
• Discrete Collaborative Filtering (2016, methodology): “Similarly, Zhou and Zha [33] rotated their learned features by running ITQ [9] to generate hash codes.”
• Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (2018, background): “Discrete hashing techniques [39, 40, 42] and binary coding of model parameters are suggested to speed up the calculation of the relevance score for a given ( q , d ) pair.”
• Collaborative Knowledge Base Embedding for Recommender Systems (2016, result): “Similar to [31], in order to be consistent with the implicit feedback setting, we extract only positive ratings (rating 5) for training and testing.”
• Learning to Hash for Indexing Big Data—A Survey (2015, background): “In addition, this emerging hash learning framework can be exploited for some general machine learning and data mining tasks, including cross-modality data fusion [139], large-scale optimization [140], largescale classification and regression [141], collaborative filtering [142], and recommendation [”