output
20132026
most citedEEG based Emotion Recognition: A Tutorial and Review

484 citations

Showing cs.IRShow all

8 papers · 1 filter

cs.IR2023★ 108 cited

Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Zhiyong Cheng, Sai Han, Fan Liu +3

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regard…

cs.IR2020★ 2 cited

Enhancing Factorization Machines with Generalized Metric Learning

Yangyang Guo, Zhiyong Cheng, Jiazheng Jing +3

Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the…

cs.IR2020★ 30 cited

Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

Shoujin Wang, Longbing Cao, Liang Hu +4

A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intr…

cs.IR2019★ 113 cited

User Diverse Preference Modeling by Multimodal Attentive Metric Learning

Fan Liu, Zhiyong Cheng, Changchang Sun +3

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. How…

cs.IR2019

Compositional Coding for Collaborative Filtering

Chenghao Liu, Tao Lu, Xin Wang +3

Efficiency is crucial to the online recommender systems. Representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computa…

cs.IR2018

Attentive Long Short-Term Preference Modeling for Personalized Product Search

Yangyang Guo, Zhiyong Cheng, Liqiang Nie +3

E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-te…

cs.IR2018★ 238 cited

MMALFM: Explainable Recommendation by Leveraging Reviews and Images

Zhiyong Cheng, Xiaojun Chang, Lei Zhu +2

Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for indiv…

cs.IR2018★ 122 cited

Attentive Aspect Modeling for Review-aware Recommendation

Xinyu Guan, Zhiyong Cheng, Xiangnan He +4

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user…