3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach
Daichi Amagata, Makoto Onizuka, Takahiro Hara
Distance-based outlier detection is widely adopted in many fields, e.g., data mining and machine learning, because it is unsupervised, can be employed in a generic metric space, an…
Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?
Daichi Amagata, Takahiro Hara
The MIPS (maximum inner product search), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. It is usu…
Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub
Shohei Tsuruoka, Daichi Amagata, Shunya Nishio +1
Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generate…