標(biāo)題: Titlebook: Knowledge Science, Engineering and Management; 13th International C Gang Li,Heng Tao Shen,Xiang Zhao Conference proceedings 2020 Springer N [打印本頁] 作者: 萬能 時(shí)間: 2025-3-21 17:38
書目名稱Knowledge Science, Engineering and Management影響因子(影響力)
書目名稱Knowledge Science, Engineering and Management影響因子(影響力)學(xué)科排名
書目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開度
書目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Knowledge Science, Engineering and Management被引頻次
書目名稱Knowledge Science, Engineering and Management被引頻次學(xué)科排名
書目名稱Knowledge Science, Engineering and Management年度引用
書目名稱Knowledge Science, Engineering and Management年度引用學(xué)科排名
書目名稱Knowledge Science, Engineering and Management讀者反饋
書目名稱Knowledge Science, Engineering and Management讀者反饋學(xué)科排名
作者: FLAG 時(shí)間: 2025-3-21 22:18 作者: 網(wǎng)絡(luò)添麻煩 時(shí)間: 2025-3-22 00:34
Parameter Optimization and Weights Assessment for Evidential Artificial Immune Recognition Systemical elements. They achieved a big success in the area of machine learning. Nevertheless, the majority of AIRS versions does not take into account the effect of uncertainty related to the classification process. Managing uncertainty is undoubtedly among the fundamental challenges in real-world class作者: indigenous 時(shí)間: 2025-3-22 06:28 作者: 概觀 時(shí)間: 2025-3-22 12:02 作者: Itinerant 時(shí)間: 2025-3-22 16:02
Pairwise-Based Hierarchical Gating Networks for Sequential Recommendationcommendation task. Most existing methods based on Markov Chains or deep learning architecture have demonstrated their superiority in sequential recommendation scenario, but they have not been well-studied at a range of problems: First, the influence strength of items that the user just access might 作者: START 時(shí)間: 2025-3-22 17:38
Time-Aware Attentive Neural Network for News Recommendation with Long- and Short-Term User Representtations is a challenging task in news recommendation. Existing methods usually utilize recurrent neural networks to capture the short-term user interests, and have achieved promising performance. However, existing methods ignore the user interest drifts caused by time interval in the short session. 作者: 易發(fā)怒 時(shí)間: 2025-3-22 22:48
A Time Interval Aware Approach for Session-Based Social Recommendationtheir interests to enhance the activeness and retention of users. Besides, their interests change from time to time. Session-based recommendation divides users’ interaction history into sessions and predict users’ behaviors with the context information in each session. It’s essential but challenging作者: moratorium 時(shí)間: 2025-3-23 04:08
AutoIDL: Automated Imbalanced Data Learning via Collaborative Filteringthods usually ignore the intrinsic imbalance nature of most real-world datasets and lead to poor performance. For handling imbalanced data, sampling methods have been widely used since their independence of the used algorithms. We propose a method named AutoIDL for selecting the sampling methods as 作者: 保守 時(shí)間: 2025-3-23 06:59
Fusion of Domain Knowledge and Text Features for Query Expansion in Citation Recommendationortant for literature reviewing, literature-based discovery and a wide range of applications. In this paper, we propose a query expansion framework via fusing domain-specific knowledge and text features for academic citation recommendation. Starting from an original query, domain-specific and contex作者: delusion 時(shí)間: 2025-3-23 11:13
Robust Sequence Embedding for Recommendationti-classification task with the historical sequence as the input, and the next item as the output class label. Sequence representation learning in the multi-classification task is of our main concern. The item frequency usually follows the long tail distribution in recommendation systems, which will作者: 灰心喪氣 時(shí)間: 2025-3-23 16:17
Deep Generative Recommendation with Maximizing Reciprocal Rankative model is that it can break through the limited modeling capabilities of linear models which dominate collaborative filtering research to a large extend. In this paper, we propose a deep generative recommendation model by enforcing a list-wise ranking strategy to VAE with the aid of multinomial作者: 沙發(fā) 時(shí)間: 2025-3-23 18:08 作者: 放逐某人 時(shí)間: 2025-3-24 01:31 作者: 徹底檢查 時(shí)間: 2025-3-24 06:11
Seeds Selection for Influence Maximization Based on Device-to-Device Social Knowledge by Reinforcemege D2D social knowledge to select influential users (seed users or seeds) for influence maximization to minimize network traffic. Lots of work has been done for seeds selection in a single community. However, few studies are about seeds selection in multiple communities. In this paper, we build a Mu作者: 沒血色 時(shí)間: 2025-3-24 09:38
CIFEF: Combining Implicit and Explicit Features for Friendship Inference in Location-Based Social Nemost active problems in LBSNs is friendship inference based on their rich check-in data. Previous studies are mainly based on co-occurrences of two users, however, a large number of user pairs have no co-occurrence, which weakens the performance of previous proposed methods. In this paper, we propos作者: Observe 時(shí)間: 2025-3-24 14:35
r Neuzeit zu eigenst?ndigen Teildisziplinen entwickelt haben: Analysis, Wahrscheinlichkeitstheorie, angewandte Mathematik, Topologie und Mengenlehre. Die Darstellung verzichtet auf Vollst?ndigkeit und konzentriert sich stattdessen ganz bewusst auf wesentliche oder besonders interessante Aspekte: Ein作者: MILK 時(shí)間: 2025-3-24 17:27 作者: IRK 時(shí)間: 2025-3-24 21:22 作者: DUCE 時(shí)間: 2025-3-24 23:13 作者: 放牧 時(shí)間: 2025-3-25 07:23 作者: Celiac-Plexus 時(shí)間: 2025-3-25 11:34 作者: tangle 時(shí)間: 2025-3-25 13:51
s zu den wenigsten mathematischen Problemen nur einen (richtigen) L?sungsweg gibt. Auch Querverbindungen zwischen den verschiedenen Disziplinen werden deutlich. Das Buch wendet sich an all jene, die eine übersichtliche, kurze Darstellung der zentralen Momente in der Geschichte der Mathematik suchen 作者: Nomogram 時(shí)間: 2025-3-25 18:50
Jingqi Zhang,Zhongbin Sun,Yong Qiss alternativer Heilmethoden und Heilserwartungen immer mehr zu einer Form der Heilkunde, in der das unwissenschaftliche Prinzip des ?Wer heilt, hat Recht!“ Oberhand gewann. Was chinesische ?rzte in jahrelangem Studium etwa der Akupunktur erlernten, wurde im Westen in Schnellkursen vermittelt Inzwis作者: beta-carotene 時(shí)間: 2025-3-25 21:37
Yanli Hu,Chunhui He,Zhen Tan,Chong Zhang,Bin Gegeworden, in Würde, Autonomie und ausschlie?lich um seiner selbst willen im Mittelpunkt von vor- und fürsorgender, heilender, wo Heilung nicht mehr m?glich ist, von palliativ-umsorgender und schlie?lich auch forschender ?rztlicher Kunst zu stehen. Die Heilkunde ist am Beginn des 21. Jahrhunderts als作者: 打包 時(shí)間: 2025-3-26 03:02
Rongzhi Zhang,Shuzi Niu,Yucheng Liranken durch die Gesunden die Medizin beherrschte. Mit dem Aufkommen des Darwinschen Evolutionismus im letzten Jahrhundert wurde es vielfach üblich, durch Analogie von der sog. primitiven Medizin auf die Anf?nge unserer eigenen zu schlie?en. In diesem Sinne wird etwa die Trepanation, die von gewisse作者: Callus 時(shí)間: 2025-3-26 04:28 作者: BUDGE 時(shí)間: 2025-3-26 09:42 作者: Brittle 時(shí)間: 2025-3-26 13:49 作者: EXULT 時(shí)間: 2025-3-26 20:00
Time-Aware Attentive Neural Network for News Recommendation with Long- and Short-Term User Representthe short-term user representations from their recently browsing news through the T-SA. In addition, we learn more informative news representations from the historical readers and the contents of news articles. Moreover, we adopt the latent factor model to build the long-term user representations fr作者: Bumble 時(shí)間: 2025-3-27 00:01 作者: ERUPT 時(shí)間: 2025-3-27 05:02 作者: 向下 時(shí)間: 2025-3-27 07:06 作者: 敲詐 時(shí)間: 2025-3-27 12:43 作者: 調(diào)味品 時(shí)間: 2025-3-27 16:30
Deep Generative Recommendation with Maximizing Reciprocal Rankring at the top of the predicted recommendation list as possible. The experimental results demonstrated that the proposed method outperforms several state-of-the-art methods in ranking estimation task.作者: LINES 時(shí)間: 2025-3-27 19:43
Spatio-Temporal Attentive Network for Session-Based Recommendationdomness of user’s behaviors, is proposed to enrich item representations. Then we utilize attention mechanism to capture the user’s real purpose involved user’s initial will and main intention. Extensive experimental results on three real-world benchmark datasets show that STASR consistently outperfo作者: 遺忘 時(shí)間: 2025-3-27 22:18
Seeds Selection for Influence Maximization Based on Device-to-Device Social Knowledge by Reinforcemee computing tasks from the remote cloud to adjacent base stations (BSs). The experiment results on a realistic D2D data set show our method improves D2D coverage by 17.65% than heuristic average allocation. The cellular network traffic is reduced by 26.35% and the time delay is reduced by 63.53%.作者: Compatriot 時(shí)間: 2025-3-28 04:46
CIFEF: Combining Implicit and Explicit Features for Friendship Inference in Location-Based Social Newe propose a new explicit feature to capture the explicit information of user pairs who have common locations. Extensive experiments on two real-world LBSNs datasets show that our proposed method CIFEF can outperform six state-of-the-art methods.作者: paragon 時(shí)間: 2025-3-28 07:16 作者: 阻擋 時(shí)間: 2025-3-28 12:46
Sili Huang,Bo Yang,Hechang Chen,Haiyin Piao,Zhixiao Sun,Yi Chang作者: Lacunar-Stroke 時(shí)間: 2025-3-28 17:17
Wencong Wang,Lan Huang,Hao Liu,Jia Zeng,Shiqi Sun,Kainuo Li,Kangping Wang作者: BRAND 時(shí)間: 2025-3-28 22:16
Gong Xudong,Jia Hongda,Zhou Xing,Feng Dawei,Ding Bo,Xu Jie作者: Mirage 時(shí)間: 2025-3-29 01:17
Shaokang Zhang,Huailiang Peng,Yanan Cao,Lei Jiang,Qiong Dai,Jianlong Tan作者: 有惡意 時(shí)間: 2025-3-29 03:55
Cheng He,Chao Peng,Na Li,Xiang Chen,Zhengfeng Yang,Zhenhao Hu作者: 名字 時(shí)間: 2025-3-29 07:36 作者: ENACT 時(shí)間: 2025-3-29 14:04 作者: Mere僅僅 時(shí)間: 2025-3-29 18:35
0302-9743 zed in the following topical sections: machine learning; recommendation algorithms and systems; social knowledge analysis and management; text mining and document analysis; and deep learning..*The conference was held virtually due to the COVID-19 pandemic..978-3-030-55392-0978-3-030-55393-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: LAY 時(shí)間: 2025-3-29 23:09
MA-TREX: Mutli-agent Trajectory-Ranked Reward Extrapolation via Inverse Reinforcement Learnings adopted in the iteration process, by which the self-generated data required subsequently is only one third of the initial demonstrations. Experimental results on several multi-agent collaborative tasks demonstrate that the MA-TREX can effectively surpass the demonstrators and obtain the same level reward as the ground truth quickly and stably.作者: Cleave 時(shí)間: 2025-3-30 00:38
An Incremental Learning Network Model Based on Random Sample Distribution Fittingial samples were mixed with new real data as training data. The experiments with proper parameters show that new features from new real data can be learned as well as the old features are not forgot catastrophically.作者: FANG 時(shí)間: 2025-3-30 06:10 作者: 粗鄙的人 時(shí)間: 2025-3-30 11:26 作者: Pandemic 時(shí)間: 2025-3-30 15:19 作者: 沒有希望 時(shí)間: 2025-3-30 19:18 作者: condescend 時(shí)間: 2025-3-31 00:37
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/544059.jpg
