一种基于协同过滤和混合相似性模型的推荐算法

针对推荐系统协同过滤方法中存在的数据稀疏和冷启动等问题,提出一种基于协同过滤和混合相似性模型的推荐算法。该算法首先计算用户在不同项目间的相似性,然后结合项目特性和标签信息权重来描述用户、项目、特性和标签之间的关系;其次,设定用户偏好因子和不对称因子调整不同用户间的评分偏好;最后,结合用户间相似性、项目综合权重,以及评分偏好构建混合相似性模型,并加入用户时间权重信息解决项目冷启动问题。在公开的MovieLens数据集上的实验表明,该算法在各种评估指标上比其他相关方法获得更显著的结果。

Recommendation algorithm based on collaborative filtering and hybrid similarity model

Like other recommendation paradigms, the algorithm based on collaborative filtering also suffers from the curse of sparse data and cold-start problem. Nevertheless, a recommendation algorithm based on collaborative filtering and hybrid similarity model was proposed. First, the similarity of users among different items was calculated by the algorithm, and then the relationship among users, items, features and tags was described according to property weights and tag weights. Next, the score preference between different users was adjusted by setting the user preference factor and the asymmetry factor. After that, a hybrid similarity model was constructed based on the user similarity, item weights and rating preferences, and the user-time weight information was also added to solve the project cold-start problem. Experiments on publicly accessible MovieLens data sets demonstrate that the algorithm achieves more prominent results than other related approaches in various evaluation metrics.