基于Apriori算法填充数据及改进相似度的推荐算法

Recommendation Algorithm Based on Apriori Algorithm and Improved Similarity

针对协同过滤算法中存在的数据稀疏和算法精确度不高的问题,提出了一种融合关联规则的协同过滤算法。首先,利用关联规则Apriori算法挖掘出用户间潜在的联系,该潜在联系采用用户间的关联规则的置信度来表示,紧接着进一步构建用户置信度矩阵,用于填充用户评分矩阵。其次,利用置信度矩阵来改进传统的相似度计算公式,构建一个用户间的综合相似度计算公式。最后,利用填充过后的用户评分矩阵和用户间的综合相似度为用户进行推荐。所提算法相比传统算法具有更高的算法精度。此外,与其他算法相比,所提算法还能有效缓解推荐系统的长尾问题,从而进一步提高推荐系统的推荐质量。

In order to alleviate the data sparse problem and improve the accuracy of collaborative filtering algorithm,a recommendation algorithm based on Apriori algorithm and improved similarity is presented.Firstly,it uses Apriori algorithm to mine the potential connections between users,and uses the confidence of the association rules between users to represent the potential connections between users,then constructs a user confidence matrix to fill the user rating matrix.Secondly,the algorithm uses the confidence matrix to improve the traditional similarity calculation formula and build a comprehensive similarity calculation formula between users.Finally,the algorithm uses the filled user rating matrix and the comprehensive similarity between users to make recommendations for users.The proposed algorithm has higher algorithm accuracy than traditional algorithms.Compared with other algorithms,the proposed algorithm can effectively alleviate the long tail problem of the recommendation system,so as to further improve the recommendation quality of the recommendation system.