论文标题
演示一个信任步行者，用于对推荐系统进行评级预测的信任步行，并随机步行：h-索引中心性的影响，物品和朋友的相似性
Presentation a Trust Walker for rating prediction in Recommender System with Biased Random Walk: Effects of H-index Centrality, Similarity in Items and Friends
论文作者
论文摘要
推荐系统的使用已大大增加，以帮助在线社交网络用户进行决策过程并选择适当的项目。另一方面，由于许多不同的项目，用户无法获得广泛的范围，通常，为用户创建的矩阵存在一个散射问题。为了解决问题，应用了基于信任的推荐系统来预测用户所需项目的分数。已经考虑了各种标准来定义信任，并且通常根据这些标准计算用户之间的信任程度。在这方面，由于社交网络中的数量很大，因此无法获得所有用户的信任程度。同样，对于这个问题，研究人员使用不同的随机步行算法模式随机访问某些用户，研究其行为并获得他们之间的信任程度。在本研究中，提出了一个基于信任的推荐系统，该系统可以预测目标用户尚未评级的项目的分数，如果找不到该项目，则为用户提供依赖于该项目的项目，这些项目也是用户兴趣的一部分。在值得信赖的网络中，通过加权节点之间的边缘，确定信任程度，并开发了信任行者，该信任人使用偏见的随机步行（BRW）算法在节点之间移动。边缘的重量在选择随机步骤中有效。本研究方法的实施和评估已在三个名为Epinions，Flixster和Filmtrust的数据集上进行。结果揭示了该方法的高效率。
The use of recommender systems has increased dramatically to assist online social network users in the decision-making process and selecting appropriate items. On the other hand, due to many different items, users cannot score a wide range of them, and usually, there is a scattering problem for the matrix created for users. To solve the problem, the trust-based recommender systems are applied to predict the score of the desired item for the user. Various criteria have been considered to define trust, and the degree of trust between users is usually calculated based on these criteria. In this regard, it is impossible to obtain the degree of trust for all users because of the large number of them in social networks. Also, for this problem, researchers use different modes of the Random Walk algorithm to randomly visit some users, study their behavior, and gain the degree of trust between them. In the present study, a trust-based recommender system is presented that predicts the score of items that the target user has not rated, and if the item is not found, it offers the user the items dependent on that item that are also part of the user's interests. In a trusted network, by weighting the edges between the nodes, the degree of trust is determined, and a TrustWalker is developed, which uses the Biased Random Walk (BRW) algorithm to move between the nodes. The weight of the edges is effective in the selection of random steps. The implementation and evaluation of the present research method have been carried out on three datasets named Epinions, Flixster, and FilmTrust; the results reveal the high efficiency of the proposed method.
