融合用户评分与显隐兴趣相似度的协同过滤推荐算法

协同过滤算法是推荐系统中使用最广泛的算法,其核心是利用某兴趣爱好相似的群体来为用户推荐感兴趣的信息。传统的协同过滤算法利用用户-项目评分矩阵计算相似度,通过相似度寻找用户的相似群体来进行推荐,但是由于其评分矩阵的稀疏性问题,对相似度的计算不够准确,这间接导致推荐系统的质量下降。为了缓解数据稀疏性对相似度计算的影响并提高推荐质量,提出了一种融合用户评分与用户显隐兴趣的相似度计算方法。该方法首先利用用户-项目评分矩阵计算用户评分相似度;然后根据用户基本属性与用户-项目评分矩阵得出项目隐性属性;之后综合项目类别属性、项目隐性属性、用户-项目评分矩阵和用户评分时间,得到用户显隐兴趣相似度;最后融合用户评分相似度和用户显隐兴趣相似度得到用户相似度,并以此相似度寻找用户的相似群体以进行推荐。在数据集Movielens上的实验结果表明,相比传统算法中仅使用单一的评分矩阵来计算相似度,提出的新相似度计算方法不仅能够更加准确地寻找到用户的相似群体,而且还能够提供更好的推荐质量。

Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest

Collaborative filtering algorithm is the most widely used algorithm in recommendation system.Its core is to use a group with similar interests to recommend information of interest for users.The traditional collaborative filtering algorithm uses the user item scoring matrix to calculate the similarity,and finds the similar groups of users through the similarity to recommend.However,due to the sparsity of the scoring matrix,the calculation of the similarity is not accurate enough,which indirectly leads to the degradation of the quality of the recommendation system.In order to alleviate the impact of data sparsity on the similarity calculation and improve the quality of recommendation,a similarity calculation method is proposed,which integrates user rating and user’s explicit and implicit interest.This method first uses the user item scoring matrix to calculate the similarity of user’s scoring,then uses the basic attribute of user and the user item scoring matrix to get the implicit attribute of project,then integrates the attribute of project category,the implicit attribute of project,the scoring matrix of user item and the scoring time of user to get the similarity of user’s explicit and implicit interest,finally integrates the similarity of user’s scoring and the similarity of user’s explicit and implicit interest to find similar groups of users for recommendation.Experimental results on the data set Movielens show that compared with the traditional algorithm,which only uses a single scoring matrix to calculate the similarity,the new similarity calculation method can not only find similar groups of users more accurately,but also provide a better recommendation quality.