基于情景上下文与信任关系的旅游景点推荐算法*

针对现有旅游景点推荐个性化的不足问题,本文提出了一种基于信任关系与于情景上下文的旅游景点推荐算法。首先在传统的协同过滤算法上以用户信任度代替相似度来解决数据稀疏性；其次引入用户情景上下文信息,更全面的反映出用户的个性化需求；最后基于用户的信任度和上下文信息优化,建立一个推荐结果准确度更高的旅游景点推荐模型。模拟实验结果表明,综合考虑信任度和情景上下文信息的推荐策略表现最优。

Attractions Recommendation AlgorithmBased on Situational Context and Trust Relationship

In consideration of the lack of personalization in the recommendation algorithms of tourist attractions, this paper proposes a recommendation algorithm of tourist attractions based on trust relationship and context. Firstly, using user trust instead of the similarity to solve the problem of data sparse based on the traditional collaborative filtering algorithm. Secondly, using user scenario context information to reflect the personalized needs of users. Finally, a recommended model of tourist attractions with higher accuracy of recommendation results is established based on user trust and contextual information optimization. The result of simulation experiment shows that the recommendation strategy which takes trust and contextual context information into consideration has the better performance than those existed strategies.