阻止
发表于 2025-3-28 18:08:49
http://reply.papertrans.cn/24/2340/233945/233945_41.png
玛瑙
发表于 2025-3-28 21:58:58
The Contemporary Russian Economyector-wise in both implicit and explicit ways, but also learn both low-order and high-order feature interactions. xDeepFM can effectively enhance recommendation accuracy. Finally, the recommendation model is embedded in the system for testing. Evaluate on the public dataset of DiDi, we compare diffe
反馈
发表于 2025-3-28 23:40:48
http://reply.papertrans.cn/24/2340/233945/233945_43.png
JIBE
发表于 2025-3-29 05:38:49
How Do Scholars and Academics Differ?ion, which requires prior knowledge to set metapaths in advance. This paper proposes a novel random-walk-based heterogeneous attention network (RHAN) for community detection on heterogeneous networks. Random walk is used to generate the neighbor nodes set of nodes, and heterogeneous information is c
GROG
发表于 2025-3-29 09:08:24
http://reply.papertrans.cn/24/2340/233945/233945_45.png
FLAG
发表于 2025-3-29 15:19:08
Computer Supported Cooperative Work and Social Computing16th CCF Conference,
IVORY
发表于 2025-3-29 16:40:43
Joint Embedding Multiple Feature and Rule for Paper Recommendations papers are recommended according to the relatedness between user interests and paper embeddings. We conduct experiments on the ACM academic paper dataset. The results show that our model outperforms baseline methods on personalized recommendation. We also analyze the influence of model structure a
混杂人
发表于 2025-3-29 22:15:43
http://reply.papertrans.cn/24/2340/233945/233945_48.png
LVAD360
发表于 2025-3-30 03:33:29
Deep Bug Triage Model Based on Multi-head Self-attention Mechanismport, and further quantifies the influence of fixers with similar activities on bug triage through fixer sequence. We conducted texts on four open source software projects. We can get the MSDBT has clear strength over the previous model in recall index.
异教徒
发表于 2025-3-30 05:25:52
Taxi Pick-Up Area Recommendation via Integrating Spatio-Temporal Contexts into XDeepFMector-wise in both implicit and explicit ways, but also learn both low-order and high-order feature interactions. xDeepFM can effectively enhance recommendation accuracy. Finally, the recommendation model is embedded in the system for testing. Evaluate on the public dataset of DiDi, we compare diffe