基于用户偏好加权的混合网络推荐算法

Hybrid recommendation by combining network-based algorithm and user preference

基于热传导或物质扩散理论的推荐算法首先利用网络结构得到对象间推荐关系,然后根据对象间关系预测用户喜欢的对象,而忽略了用户偏好。为了弥补这个缺陷,根据用户已选择对象的标签,利用TF-IDF方法构建用户偏好模型,以用户在预测对象标签上的平均偏好作为对该对象的偏好程度,采用加权方法与现有基于网络推荐算法混合运算。经在基准数据集Movie Lens上测试表明,通过与目前效果最好的几种基于网络推荐算法进行加权混合运算,推荐结果在推荐精度、个性化、多样化等多种评价指标方面均比原有算法有明显提高。

Recommendation algorithms based on heat conduction or mass diffusion first obtain the relationship between objects according to network structure,then predict the user＇s favorite objects based on these relationships,but these algorithms ignore user＇s preference. In order to overcome this defect,the TF-IDF approach was used to construct user＇s preference according to the tags contained in the objects selected by user,and the mean of preference of object＇s tags was taken as the preference of the object,then a hybrid recommendation model was proposed by combining networkbased algorithm and the user preference model. The benchmark datasets,M ovie Lens,was used to evaluate our algorithm,and the experimental results demonstrate that hybrid algorithm can significantly improve accuracy,diversification and personalization of recommendations.