Trust-aware recommender systems
Trust-aware recommender systems
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DOI：
10.1145/1297231.1297235
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发表时间：
2007-10
期刊：
影响因子：
--
通讯作者：
P. Massa;P. Avesani
中科院分区：
文献类型：
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作者：
P. Massa;P. Avesani
基于协同过滤的推荐系统向用户推荐他们可能喜欢的项目。然而，由于输入评分矩阵的数据稀疏性，寻找相似用户的步骤常常失败。我们建议使用信任度量来替换此步骤，信任度量是一种能够在信任网络上传播信任并估计可用于代替相似性权重的信任权重的算法。对 Epinions.com 数据集的实证评估表明，利用信任信息的推荐系统在准确性方面最有效，同时保持良好的覆盖范围。这对于提供很少评级的用户来说尤其明显。
Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.