CLOG
发表于 2025-3-21 16:59:06
书目名称Information Retrieval影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0465192<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=BK0465192<br><br> <br><br>书目名称Information Retrieval网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0465192<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=BK0465192<br><br> <br><br>书目名称Information Retrieval被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0465192<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=BK0465192<br><br> <br><br>书目名称Information Retrieval年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0465192<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=BK0465192<br><br> <br><br>书目名称Information Retrieval读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0465192<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=BK0465192<br><br> <br><br>
overrule
发表于 2025-3-21 22:01:54
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habitat
发表于 2025-3-22 02:35:33
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/465192.jpg
反省
发表于 2025-3-22 06:30:00
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增长
发表于 2025-3-22 10:13:09
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anthropologist
发表于 2025-3-22 15:46:36
Mining User Profiles from Query Logon. We evaluated this framework using a real search engine data set, which contains 40,000 labeled users with age, gender, and education level profiles. The experiment results demonstrated the effectiveness of our proposed model.
长处
发表于 2025-3-22 21:05:58
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改变立场
发表于 2025-3-22 23:30:49
Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion paper investigates how to enhance the simplicity of KGC model and achieve a reasonable balance between accuracy and complexity. Extensive experiments show that the proposed framework improves the performance of the current represent learning models for KGC task.
daredevil
发表于 2025-3-23 02:05:29
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老人病学
发表于 2025-3-23 05:52:58
Multi-granularity Convolutional Neural Network with Feature Fusion and Refinement for User Profilingased on the dataset from SMP CUP 2016 competition. The experimental results demonstrate that the proposed model is effective in automatic user attribute classification with a particular focus on fine-grained user information.