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Found 10 papers, 5 papers with code
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph
Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.
Enhancing Content-based Recommendation via Large Language Model
However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains?
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Graph contrastive learning (GCL) has recently achieved substantial advancements.
Contrastive Cross-Domain Sequential Recommendation
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.
Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.
Deep Structural Point Process for Learning Temporal Interaction Networks
A temporal interaction network consists of a series of chronological interactions between users and items.
Bipartite Graph Embedding via Mutual Information Maximization
However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved.
HIN: Hierarchical Inference Network for Document-Level Relation Extraction
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
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Relation Extraction
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