Journals

• Kohei Hirata, Daichi Amagata, and Takahiro Hara. Cardinality Estimation in Inner Product Space IEEE Open Journal of the Computer Society, volume 3, pages 208-216, 2022. Code is here.
• Zhi Li, Daichi Amagata, Yihong Zhang, Takuya Maekawa, Takahiro Hara, Kei Yonekawa, and Mori Kurokawa. HML4Rec: Hierarchical Meta-Learning for Cold-Start Recommendation in Flash Sale E-commerce Knowledge-based Systems, volume 255, pages 109674, 2022.
• Ryosuke Taniguchi, Daichi Amagata, and Takahiro Hara. Efficient Retrieval of Top-k Weighted Triangles on Static and Dynamic Spatial Data IEEE Access, volume 10, pages 55298-55307, 2022. Extended version of DASFAA2022.
• Daichi Amagata, Makoto Onizuka, and Takahiro Hara. Fast, Exact, and Parallel-friendly Outlier Detection Algorithms with Proximity Graph in Metric Spaces The VLDB Journal, volume 31, number 4, pages 797-821, 2022. Extended version of SIGMOD2021. Code is here.
• Shunya Nishio, Daichi Amagata, and Takahiro Hara. Lamps: Location-Aware Moving Top-k Pub/Sub IEEE Transactions on Knowledge and Data Engineering (TKDE), volume 34, number 1, pages 352-364, 2022.

Conferences

• Daichi Amagata. Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data Proceedings of the IEEE International Conference on Big Data, pages 445-454, 2022. Code is here.
• Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, and Mori Kurokawa. Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations Proceedings of the IEEE International Conference on Big Data, pages 762-769, 2022. Full version is here.
• Ryosuke Taniguchi, Daichi Amagata, and Takahiro Hara. Retrieving Top-N Weighted Spatial k-cliques Proceedings of the IEEE International Conference on Big Data, pages 4942-4951, 2022. The 7th IEEE International Workshop on Big Spatial Data.
• Daichi Amagata, Yusuke Arai, Sumio Fujita, and Takahiro Hara. Learned k-NN Distance Estimation Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information System (SIGSPATIAL), pages 1:1-1:4, 2022. The first and second authors contributed equally to this research. Full version is here. Code is here.
• Kohei Hirata, Daichi Amagata, Sumio Fujita, and Takahiro Hara. Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively Proceedings of the ACM Recommender Systems Conference (RecSys), pages 198-207, 2022. ACM Showcase on Kudos. Code is here.
• Zhi Li, Daichi Amagata, Takuya Maekawa, Kei Yonekawa, Mori Kurokawa, and Takahiro Hara. Trends-enhanced Attention & Memory Networks for E-commerce Recommendation SIGIR Workshop On eCommerce.
• Ryosuke Taniguchi, Daichi Amagata, and Takahiro Hara. Efficient Retrieval of Top-k Weighted Spatial Triangles Proceedings of International Conference on Database Systems for Advanced Applications (DASFAA), pages 224-231, 2022.
• Yuchen Ji, Daichi Amagata, Yuya Sasaki, and Takahiro Hara. A Performance Study of One-dimensional Learned Cardinality Estimation Proceedings of International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP), pages 86-90, 2022.