Bipartite graph convolutional hashing for effective and efficient top-n search in hamming space
Searching on bipartite graphs is basal and versatile to many real-world Web applications,
eg, online recommendation, database retrieval, and query-document searching. Given a …
eg, online recommendation, database retrieval, and query-document searching. Given a …
WSFE: wasserstein sub-graph feature encoder for effective user segmentation in collaborative filtering
Maximizing the user-item engagement based on vectorized embeddings is a standard
procedure of recent recommender models. Despite the superior performance for item …
procedure of recent recommender models. Despite the superior performance for item …
A survey on graph embedding techniques for biomedical data: Methods and applications
As a result of the expeditious advancement of biomedical technologies, a plethora of
relational data linking biomedical entities such as genes, proteins, and drugs have been …
relational data linking biomedical entities such as genes, proteins, and drugs have been …
Think rationally about what you see: Continuous rationale extraction for relation extraction
Relation extraction (RE) aims to extract potential relations according to the context of two
entities, thus, deriving rational contexts from sentences plays an important role. Previous …
entities, thus, deriving rational contexts from sentences plays an important role. Previous …
An effective post-training embedding binarization approach for fast online top-k passage matching
With the rapid development of Natural Language Understanding for information retrieval,
fine-tuned deep language models, eg, BERT-based, perform remarkably effective in …
fine-tuned deep language models, eg, BERT-based, perform remarkably effective in …
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
The classic problem of node importance estimation has been conventionally studied with
homogeneous network topology analysis. To deal with practical network heterogeneity, a …
homogeneous network topology analysis. To deal with practical network heterogeneity, a …
[HTML][HTML] Topological representation learning for e-commerce shopping behaviors
Learning compact representation from customer shopping behaviors is at the core of web-
scale E-commerce recommender systems. At Amazon, we put great efforts into learning …
scale E-commerce recommender systems. At Amazon, we put great efforts into learning …
Discrete Listwise Content-aware Recommendation
To perform online inference efficiently, hashing techniques, devoted to encoding model
parameters as binary codes, play a key role in reducing the computational cost of content …
parameters as binary codes, play a key role in reducing the computational cost of content …
Knowledge filter contrastive learning for recommendation
B Xia, J Qin, L Han, A Gao, C Ma - Knowledge and Information Systems, 2024 - Springer
Abstract Knowledge graph-based recommender systems integrate user–item interactions
with knowledge graph information through Graph Neural Networks (GNNs), demonstrating …
with knowledge graph information through Graph Neural Networks (GNNs), demonstrating …