基于双层相似度的协同过滤推荐算法

针对由于用户评价矩阵的数据稀疏性而导致推荐精度和准确率不高的问题，提出了一种基于双层相似度的协同过滤算法。经典算法通过改进某一种相似度或者混合相似度来提高推荐精度和准确度，本文对此进行了改进，将最近邻相似度和最近评分相似度两个概念进行区分，采用双层相似度来寻找这两个概念层次的邻居。第1层用来寻找与用户行为偏好的最近邻居，基于用户共同评价行为和差异行为的对数似然比及用户物品属性偏好相似性来实现。第2层用来寻找在评分意义上的最近评分邻居，通过改进的皮尔森相似度衡量用户评分上的相似性，给用户未知的物品进行评分预测。在Movielens数据集上的实验结果表明，本文算法能够快速排除干扰找到用户邻居，极大地提高了推荐系统的精确度、准确率。

Collaborative Filtering Algorithm Based on Bi-level Similarity

A novel bi-level similarity collaborative filtering (BLSCF) algorithm is proposed in this paper to deal with the problem of low recommendation precision and accuracy of collaborative filtering algorithms resulted from the data sparsity of user evaluation matrix. Different from the existing algorithms which improve the recommendation precision and accuracy via modifying the similarity or mixed similarity, this paper distinguishes the nearest neighborhood similarity from the nearest score similarity and utilizes the bi-level similarity to search their neighbors. The first level is to obtain the nearest neighbors of the user''s behavior preference by integrating the log-likelihood ratio of the user''s common and difference scoring behaviors with the user''s preference similarity on item attributes. The second one will search the nearest rating neighbors and measure the similarity of the user''s score via the improved Pearson similarity so as to rate and predict the user''s unknown items. It is shown via experimental results on the Movielens dataset that the proposed BLSCF algorithm in this work can rapidly eliminate the interference to find the user''s neighbor and greatly improve the accuracy and precision of the recommendation system.