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Kernel Searching Strategy for Recommender Searching Mechanism

机译：推荐人搜索机制的内核搜索策略

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A trust-aware recommender system (TARS) is widely used in social media to find useful information. Recommender searching mechanism is an important research issue in TARS. We propose a new searching strategy for recommender searching mechanism of TARS, which named kernel searching strategy. A kernel, which consists of hub nodes of the trust network, is involved in trust propagations. The kernel can be obtained from node degree or node betweenness, take these hub nodes as active users and then finds the recom-menders via trust propagations from the kernel, most of the nodes in the network will be covered. Comparing the results of these two methods, the coverage rate of these hub nodes which is obtained from the node degree is almost less than that obtained from the node betweenness. To get better coverage rate, we take both degree and betweenness into consideration. The results show that the combination can get better coverage rate only compared with the node degree. However, the combination has better convergence effect compared with the node betweenness.
机译：信任感知推荐系统（TARS）在社交媒体中广泛使用，以查找有用的信息。推荐人搜索机制是TARS研究的重要课题。针对TARS的推荐者搜索机制，提出了一种新的搜索策略，即内核搜索策略。由信任网络的集线器节点组成的内核参与信任传播。可以从节点级别或节点之间的关系中获取内核，将这些集线器节点作为活动用户，然后通过来自内核的信任传播找到建议，网络中的大多数节点都将被覆盖。比较这两种方法的结果，从节点度获得的这些集线器节点的覆盖率几乎小于从节点中间性获得的覆盖率。为了获得更好的覆盖率，我们同时考虑了程度和中间性。结果表明，与节点度相比，该组合可以获得更好的覆盖率。但是，与节点间的关系相比，该组合具有更好的收敛效果。

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