基于加权二部图的个性化方案推荐

针对传统协同过滤算法难以解决数据稀疏性、冷启动及用户兴趣各异的问题,提出了基于加权二部图的个性化推荐方法,解决个性化设计方案推荐问题。采用加权二部图,基于用户特征和方案特征的评分,对用户和方案分类,减轻数据稀疏性,形成用户-方案规则库;采用加权网络的协同过滤算法,计算新用户特征与用户-方案规则库中用户特征的改进相似度,通过Top-N方法筛选高相似的方案集进行推荐,解决冷启动和用户兴趣各异的问题。最后与传统协同过滤算法、加权二部图个性化推荐进行比较,证明该方法的有效性和实用性。

Personalized Solution Based on Weighted Bipartite Graphs

As the most widely used recommendation algorithm, the traditional collaborative filtering can hardly solve the problems of data sparsity, cold start and different user interests. Aiming at these three problems, a personalized recommendation method based on weighted bipartite graphs was proposed to solve the problem of recommendation of personalized design scheme. A weighted bipartite graph was used to classify users and schemes based on user characteristics and scoring features to reduce the data sparsity and form a user-scheme rule base. A collaborative filtering algorithm based on weighted networks was used to calculate the improved similarity of user features between new user characteristics and user-scheme rules in the library, and recommended by the Top-N method to screen high similar solution sets to solve the problems of cold starting and different user interests. Finally, compared with the traditional collaborative filtering algorithm and weighted bipartite graph, the validity and practicability of the method were proved.