2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
Siyu Wang, Xiaocong Chen, Quan Z. Sheng +2
Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex…
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations
Zhi Li, Daichi Amagata, Yihong Zhang +4
Deep learning-based recommender systems may lead to over-fitting when lacking training interaction data. This over-fitting significantly degrades recommendation performances. To ad…
Using Social Media Background to Improve Cold-start Recommendation Deep Models
Yihong Zhang, Takuya Maekawa, Takahiro Hara
In recommender systems, a cold-start problem occurs when there is no past interaction record associated with the user or item. Typical solutions to the cold-start problem make use…
A General Method for Event Detection on Social Media
Yihong Zhang, Masumi Shirakawa, Takahiro Hara
Event detection on social media has attracted a number of researches, given the recent availability of large volumes of social media discussions. Previous works on social media eve…