烤问
发表于 2025-3-21 16:21:32
书目名称Information Retrieval影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0465190<br><br> <br><br>书目名称Information Retrieval读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0465190<br><br> <br><br>
FER
发表于 2025-3-21 23:55:33
,ID-Agnostic User Behavior Pre-training for Sequential Recommendation,ser behavior sequences based on item IDs. However, this kind of approach highly relies on user-item interaction data and neglects the attribute- or characteristic-level correlations among similar items preferred by a user. In light of these issues, we propose ., which stands for .-.gnostic User Beha
抛弃的货物
发表于 2025-3-22 00:57:31
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Pulmonary-Veins
发表于 2025-3-22 06:21:45
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Frenetic
发表于 2025-3-22 08:55:17
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inconceivable
发表于 2025-3-22 14:29:03
,InDNI: An Infection Time Independent Method for Diffusion Network Inference,l role it plays in some real applications, such as rumor-spread forecasting and epidemic controlling. Most existing methods tackle the task with exact node infection time. However, collecting infection time information is time-consuming and labor-intensive, especially when information flows are huge
分开
发表于 2025-3-22 18:42:54
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Bravura
发表于 2025-3-23 00:48:11
,A Learnable Graph Convolutional Neural Network Model for Relation Extraction,ncies relevant to named entities. Recently, graph convolutional neural networks have shown great potential in supporting this task, wherein dependency trees are usually adopted to learn semantic dependencies between entities. However, the requirement of external toolkits to parse sentences poses a p
大酒杯
发表于 2025-3-23 04:03:35
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使迷醉
发表于 2025-3-23 09:25:57
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