TrustWalker: a random walk model for combining trust-based and item-based recommendation
TrustWalker: a random walk model for combining trust-based and item-based recommendation
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DOI：
10.1145/1557019.1557067
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Mohsen Jamali;M. Ester
中科院分区：
文献类型：
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作者：
Mohsen Jamali;M. Ester
协作过滤是构建推荐系统的最受欢迎的方法，并且已成功地用于许多应用程序中。但是，它不能为仅评级很少数量项目的所谓冷启动用户提出建议。此外，这些方法不知道他们在建议中有多么自信。基于信任的推荐方法假设用户之间信任网络的附加知识，并且可以更好地与冷启动用户交易，因为用户只需要简单地连接到信任网络即可。另一方面，用户项目评级的稀疏性迫使基于信任的方法考虑仅受信任的间接邻居的评级，这可能会降低其精度。为了找到一个良好的权衡，我们提出了一个随机步行模型，该模型结合了基于信任的和协作过滤方法进行推荐。随机步行模型使我们能够定义并测量建议的信心。我们对Epinions数据集进行了评估，并将我们的模型与现有基于信任的和协作过滤方法进行了比较。
Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items. In addition, these methods do not know how confident they are in their recommendations. Trust-based recommendation methods assume the additional knowledge of a trust network among users and can better deal with cold start users, since users only need to be simply connected to the trust network. On the other hand, the sparsity of the user item ratings forces the trust-based approach to consider ratings of indirect neighbors that are only weakly trusted, which may decrease its precision. In order to find a good trade-off, we propose a random walk model combining the trust-based and the collaborative filtering approach for recommendation. The random walk model allows us to define and to measure the confidence of a recommendation. We performed an evaluation on the Epinions dataset and compared our model with existing trust-based and collaborative filtering methods.