基于景点标签的协同过滤推荐

针对基于用户社会关系的协同过滤推荐算法有时无法给出目标用户对目标物品的评分的情况，以及基于物品的协同过滤推荐算法中存在的用户对不同类型物品的评分可能不具有可比性的问题，提出了两个基于物品标签的协同过滤推荐算法。这两个算法在计算物品相似度时引入了物品的类型标签信息。在景点评分数据上的实验结果表明：相比基于用户社会关系的协同过滤推荐算法，基于用户社会关系和物品标签的协同过滤推荐算法的准确率和覆盖率提升最高达10%和4%；相比基于物品的协同过滤推荐算法，基于物品和物品标签的协同过滤推荐算法的准确率提升达15%。这说明景点类型标签信息的引入能使得景点的相似度计算更准确。

Collaborative filtering recommendation based on tags of scenic spots

In user-based collaborative filtering recommendation based on social relations, sometimes the ratings for the target items can not be predicted. Whats more, in traditional item-based collaborative filtering, there are still some items which are not in the same class with the target item and not suitable to be references for predicting ratings. To handle these problems, two new algorithms of collaborative filtering recommendation were proposed, in which the tags of scenic spots type were introduced to compute the similarity between two scenic spots. The experimental results on the data set of scenic spots ratings show that, compared with the user-based collaborative filtering recommendation algorithms based on social relations, the algorithm based on the social relation and tag can increase the accuracy and the coverage by 10% and 4% respectively, and compared with the item-based collaborative filtering recommendation algorithms, the collaborative filtering recommendation algorithm based on item and tag can increase the accuracy by 15%, it also shows that introducing the tags of scenic spots type can make the computation of the similarity between two scenic spots more accurate.