A topic-biased user reputation model in rating systems
A topic-biased user reputation model in rating systems
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DOI：
10.1007/s10115-014-0780-9
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发表时间：
2015-09-01
影响因子：
2.7
通讯作者：
Yu, Jeffrey Xu
中科院分区：
文献类型：
--
作者：
Li, Baichuan;Li, Rong-Hua;Yu, Jeffrey Xu
在像Epinions和亚马逊的产品评论系统这样的评级系统中，用户对不同主题的商品进行评级，从而得出商品分数。传统上，项目得分是通过对所有权重相等的评分进行平均来估计的。为了提高估算项目得分的准确性，用户声誉[又名。用户声誉（user reputation， UR）]。然而，UR上现有的算法低估了主题在评级系统中的作用。在本文中，我们首先通过实证调查揭示了UR具有主题偏倚。然而，现有的算法无法在评级系统中捕捉到这一特征。为了解决这个问题，我们提出了一个主题偏倚模型（TBM）来根据不同的主题和项目得分来估计UR。使用TBM，我们开发了六种主题偏向算法，随后通过使用真实世界和合成数据集的实验对其进行了评估。实验结果表明，主题偏向算法有效地估计了不同主题之间的UR，并产生了比以前基于声誉的算法更稳健的项目分数，从而可能导致更稳健的评级系统。
In rating systems like Epinions and Amazon's product review systems, users rate items on different topics to yield item scores. Traditionally, item scores are estimated by averaging all the ratings with equal weights. To improve the accuracy of estimated item scores, user reputation [a.k.a., user reputation (UR)] is incorporated. The existing algorithms on UR, however, have underplayed the role of topics in rating systems. In this paper, we first reveal that UR is topic-biased from our empirical investigation. However, existing algorithms cannot capture this characteristic in rating systems. To address this issue, we propose a topic-biased model (TBM) to estimate UR in terms of different topics as well as item scores. With TBM, we develop six topic-biased algorithms, which are subsequently evaluated with experiments using both real-world and synthetic data sets. Results of the experiments demonstrate that the topic-biased algorithms effectively estimate UR across different topics and produce more robust item scores than previous reputation-based algorithms, leading to potentially more robust rating systems.