210 citations · 584 across the 37 of their papers we have counts for
9 papers · 1 filter
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
Jiangnan Xia, Yu Yang, Senzhang Wang +3
POI recommendation is practically important to facilitate various Location-Based Social Network services, and has attracted rising research attention recently. Existing works gener…
Geometric Interaction Augmented Graph Collaborative Filtering
Yiding Zhang, Chaozhuo Li, Senzhang Wang +2
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly resear…
Interpretable Deep Learning Model for Online Multi-touch Attribution
Dongdong Yang, Kevin Dyer, Senzhang Wang
In online advertising, users may be exposed to a range of different advertising campaigns, such as natural search or referral or organic search, before leading to a final transacti…
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
Chuan Shi, Xiaotian Han, Li Song +4
Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing l…
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
Hao Peng, Jianxin Li, Qiran Gong +5
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text clas…
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
Jianping Cao, Senzhang Wang, Danyan Wen +3
Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be ``comparative'' since they m…
Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping
Xinghua Wang, Zhaohui Peng, Senzhang Wang +3
Collaborative Filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting a user's preference to the items in a single dom…
Ensemble Neural Relation Extraction with Adaptive Boosting
Dongdong Yang, Senzhang Wang, Zhoujun Li
Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and no…
Location Prediction of Social Images via Generative Model
Xiaoming Zhang, Zhoujun Li, Senzhang Wang +2
The vast amount of geo-tagged social images has attracted great attention in research of predicting location using the plentiful content of images, such as visual content and textu…