摘要 :
Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields...
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Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.
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摘要 :
基于缩小未反应核模型,考虑氧化层对氧气的扩散影响,构建了空气中 Fe 颗粒的非均相着火模型.模型计算的着火温度与实验着火温度的最大误差为 6.95%,与 Mi 等的着火模型对比,两者所计算的颗粒升温速率基本一致,着火延迟时间的数量级相同.基于该模型研究...
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基于缩小未反应核模型,考虑氧化层对氧气的扩散影响,构建了空气中 Fe 颗粒的非均相着火模型.模型计算的着火温度与实验着火温度的最大误差为 6.95%,与 Mi 等的着火模型对比,两者所计算的颗粒升温速率基本一致,着火延迟时间的数量级相同.基于该模型研究了环境压强、氧气浓度、初始氧化层厚度和颗粒粒径对着火温度、着火延迟的影响.结果表明:着火温度、着火延迟时间随环境压强、氧气浓度增大而减小,随初始氧化层厚度增厚而增大;粒径小于 10µm的范围内,着火温度随粒径减小而增大,粒径在 10～50µm范围内,粒径对着火温度影响不大;随着粒径增加,着火延迟时间增加.
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