基于社会网络分析的旅游产品推荐方法研究
发布时间：2018-10-23 21:28
【摘要】：利用社会网络分析技术,构建出游客社会网络,挖掘出游客间存在的局部社区关系,然后与传统的协同过滤推荐算法进行结合,能够有效解决旅游产品推荐中数据稀疏性问题,另一方面向有社会关系的一群用户推荐他们喜欢的旅游产品,能够减少推荐的盲目性,提高推荐精准度,并且能够协助旅游公司给用户提供更加个性化的服务,提升旅游体验。为解决游客社会网络构建与社区发现问题,以真实的游客旅游记录为基础,设计出了一种游客社会网络构建方法,并研究了基于中心节点扩张的局部社区挖掘算法。该算法对PageRank算法进行了修改,使其适用于社会网络中节点的排名,在此基础上研究了基于中心节点扩张的局部社区挖掘算法。在基于社会网络分析的旅游产品推荐算法中,通过计算同一局部社区内用户之间的直接信任度和间接信任度,量化为信任度值,然后对传统的协同过滤推荐算法进行改进,将用户的信任度值融入到用户相似性的计算当中去。通过对某旅游公司的真实游客旅游记录进行加载、转换和去噪,进行实验。实验结果表明,本文提出的基于中心节点扩张的局部社区挖掘算法可以有效地挖掘出游客社会网络中存在的局部社区,并且具有较小的时间复杂度。在推荐算法的对比实验当中,本文采用平均绝对误差MAE与准确率作为对比,对比结果表明,本文提出的基于社会网络分析的旅游产品推荐算法的MAE比传统的协同过滤推荐算法降低了0.021,准确率提高了2.5%。
[Abstract]:By using the social network analysis technology, the tourist social network can be constructed, the local community relationship among tourists can be excavated, and then combined with the traditional collaborative filtering recommendation algorithm, it can effectively solve the problem of data sparsity in tourism product recommendation. On the other hand, the recommendation of tourism products to a group of users with social relationship can reduce the blindness of recommendation, improve the accuracy of recommendation, and help travel companies to provide more personalized services to users and enhance the tourism experience. In order to solve the problem of social network construction and community discovery, a method of constructing tourist social network is designed based on the real tourist travel records, and the local community mining algorithm based on the expansion of central node is studied. The algorithm modifies the PageRank algorithm and makes it applicable to the ranking of nodes in the social network. On this basis, the local community mining algorithm based on the expansion of central nodes is studied. In the tourism product recommendation algorithm based on social network analysis, the direct trust and indirect trust between users in the same local community are calculated, and then the traditional collaborative filtering recommendation algorithm is improved. The trust value of the user is incorporated into the calculation of the user similarity. By loading, converting and de-noising the real tourist travel records of a tourism company, the experiment is carried out. The experimental results show that the proposed local community mining algorithm based on the expansion of central nodes can effectively mine the local communities existing in the tourist social network and has a relatively small time complexity. In the comparison experiment of the recommendation algorithm, the average absolute error (MAE) is compared with the accuracy rate. The comparison results show that, The MAE of tourism product recommendation algorithm based on social network analysis in this paper is 0.021 less than the traditional collaborative filtering recommendation algorithm, and the accuracy is improved 2.5%.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
，
本文编号：2290514
[Abstract]:By using the social network analysis technology, the tourist social network can be constructed, the local community relationship among tourists can be excavated, and then combined with the traditional collaborative filtering recommendation algorithm, it can effectively solve the problem of data sparsity in tourism product recommendation. On the other hand, the recommendation of tourism products to a group of users with social relationship can reduce the blindness of recommendation, improve the accuracy of recommendation, and help travel companies to provide more personalized services to users and enhance the tourism experience. In order to solve the problem of social network construction and community discovery, a method of constructing tourist social network is designed based on the real tourist travel records, and the local community mining algorithm based on the expansion of central node is studied. The algorithm modifies the PageRank algorithm and makes it applicable to the ranking of nodes in the social network. On this basis, the local community mining algorithm based on the expansion of central nodes is studied. In the tourism product recommendation algorithm based on social network analysis, the direct trust and indirect trust between users in the same local community are calculated, and then the traditional collaborative filtering recommendation algorithm is improved. The trust value of the user is incorporated into the calculation of the user similarity. By loading, converting and de-noising the real tourist travel records of a tourism company, the experiment is carried out. The experimental results show that the proposed local community mining algorithm based on the expansion of central nodes can effectively mine the local communities existing in the tourist social network and has a relatively small time complexity. In the comparison experiment of the recommendation algorithm, the average absolute error (MAE) is compared with the accuracy rate. The comparison results show that, The MAE of tourism product recommendation algorithm based on social network analysis in this paper is 0.021 less than the traditional collaborative filtering recommendation algorithm, and the accuracy is improved 2.5%.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
，
本文编号：2290514
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