【摘要】 推荐系统中,基于聚类的协同过滤推荐算法利用K-means等算法对用户和物品进行聚类,聚类结果里用户或物品只能属于一个类别,然而在实际应用中,一个用户可以有多种兴趣,一个物品也可以属于多个类别。针对上述问题,提出了一种基于谱聚类群组发现的算法,该算法通过谱聚类和C-means聚类得到用户和物品相似度较高的群组以及用户和物品归属于群组的隶属度矩阵,而且用户或物品可以属于多个群组。通过计算用户在各个群组中对物品的偏好值,并结合用户和物品在群组里相应的隶属度来预测用户对物品最终的偏好值,生成对用户的Top-N推荐结果。实验结果表明,与以往推荐算法相比,本方法在降低了数据稀疏性的同时提高了推荐结果的准确率和召回率。更多还原

【Abstract】 In many recommendation systems,Collaborative filtering recommendation algorithms based on clustering use some specific algorithms such as the K-means algorithm to cluster the users and items,but the limit is that a user or item can only belong to one category in the clustering result.In practical application,a user may have a variety of interests and an item also belongs to multiple categories.To solve the above problem,this paper put forward a novel algorithm based spectral clustering subgroups discovering and C-means clustering,by which we got the user-item subgroups with a high degree of similarities and the membership matrix of subgroups of users and items,which can belong to multiple subgroups.The purpose of our algorithm is to predict the users’ final preference to the items by calculating user’s preference to the items in each subgroup and combining the corresponding membership of users and items in their subgroup,and generate the users’ top-N recommendation results.Experimental results show that our method reduces the data sparseness and improves the recommendation precision and recall compared with previous recommendation algorithms.更多还原