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Information Recommendation Method Research Based on Trust Network and Collaborative Filtering

机译：基于信任网络和协同过滤的信息推荐方法研究

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摘要

Information recommender system is considered to be one of the most effective tools to solve the problem of information overload. Collaborative Filtering (CF), which utilizes similar neighbors to generate recommendations, is believed to be the most widely implemented and most mature technique for recommender systems. However, the recommendation results are often unsatisfactory due to the data sparsity of the input ratings matrix. Consequently, a hybrid recommender system which combines social network, trust network, and improved CF is proposed to enhance the accuracy of recommendation and overcome the weakness of data sparsity. Another advantage of the system is that utilizing the community structure discovered in social network as a new trust network sharply reduces the computation required for traditional CF. An empirical evaluation on Epinions.com dataset shows that the hybrid recommender system which incorporates social network and trust network into improved CF is more effective in terms of accuracy. This is especially evident on users who provided few ratings.
机译：信息推荐系统被认为是解决信息过载问题的最有效工具之一。协作过滤（CF）利用相似的邻居来生成推荐，被认为是推荐系统中应用最广泛，最成熟的技术。但是，由于输入评级矩阵的数据稀疏性，推荐结果通常不能令人满意。因此，提出了一种结合了社交网络，信任网络和改进的CF的混合推荐系统，以提高推荐的准确性并克服数据稀疏性的缺点。该系统的另一个优点是，利用在社交网络中发现的社区结构作为新的信任网络，可以大大减少传统CF所需的计算量。对Epinions.com数据集的经验评估表明，将社交网络和信任网络纳入改进的CF的混合推荐系统在准确性方面更为有效。这对于提供很少评分的用户尤其明显。

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