信任模型中虚假推荐过滤算法的改进

针对虚假推荐过滤算法中的利用恶意推荐值大小对恶意节点进行排序从而降低算法的有效性问题，提出一种虚假过滤改进算法。通过判断恶意节点对最终信任值的影响因素，得出恶意节点偏离平均值的标准差越大，对最后的信任值影响也越大，并根据节点推荐的标准差大小给出过滤虚假推荐节点的顺序。与已有算法相比，在具有大量的恶意推荐时，该改进算法能更好地过滤掉虚假推荐信任。

Improvement of Unfair Recommendation Filtering Algorithm in Trust Model

Aiming at the lack of malicious nodes order in unfair recommendation filtering algorithm, through the judgment which is important factor in final trust value, this paper finds malicious nodes, the greater the standard deviation of malicious nodes, the greater of impact final trust value, gives an order of filtering malicious nodes according to the standard deviation. The algorithm is compared with algorithm of Mui et al, when there are a large number of malicious recommendations, the improved algorithm can better filter out unfair recommendation trust.