Over the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

在过去的十年中，推荐系统一直是各种研究人员最追捧的系统之一。对在线系统的客户进行篮子分析并向他们推荐有吸引力的产品（电影）非常重要。向客户提供有吸引力的电影和最喜欢的电影将提高销售率并最终改善系统。迄今为止，已经提出了各种方法来分析顾客购物篮并提供娱乐电影，但是每种所提出的方法都存在挑战，例如缺乏准确性和推荐的高误差。在本文中，基于链接预测的方法被用来应对其他方法的挑战。本文提出的方法包括四个阶段：（1）运行CBRS，在此阶段，使用基于密度的应用程序空间聚类和噪声算法（DBScan）对所有用户进行聚类，并使用深度神经网络（DNN）算法对新用户进行分类。（2）基于混合相似性准则的协作推荐系统（CRS），通过该准则，将基于新用户与所选类别中的用户之间的阈值（lambda）计算相似性。相似性标准是根据年龄，性别和职业确定的。协作推荐系统提取与新用户最相似的用户。然后，基于邻接矩阵向新用户建议更高级别的电影服务。（3）在数据集上运行改进的Friendlink算法，以计算通过链接连接的用户之间的相似度。（4）此阶段与协作推荐系统的输出和改进的Friendlink算法相结合。结果表明，与朴素贝叶斯，多属性决策树和随机算法等基本模型相比，所提模型的均方误差（MSE）分别降低了8.59％，8.67％，8.45％和8.15％。此外，与SVD相比，所提出方法的平均绝对误差（MAE）降低了约4.5％，而与ApproSVD相比，降低了约4.4％，与SVD相比，所提出方法的均方根误差（RMSE）降低了6.05％，而与SVD相比降低了约6.02％与ApproSVD相比。与朴素贝叶斯（Naive Bayes），多属性决策树和随机算法等基本模型相比，降低了15％。此外，与SVD相比，所提出方法的平均绝对误差（MAE）降低了约4.5％，而与ApproSVD相比，降低了约4.4％，与SVD相比，所提出方法的均方根误差（RMSE）降低了6.05％，而与SVD相比降低了约6.02％与ApproSVD相比。与朴素贝叶斯（Naive Bayes），多属性决策树和随机算法之类的基本模型相比，降低了15％。此外，与SVD相比，所提出方法的平均绝对误差（MAE）降低了约4.5％，而与ApproSVD相比，降低了约4.4％，与SVD相比，所提出方法的均方根误差（RMSE）降低了6.05％，而与SVD相比降低了约6.02％与ApproSVD相比。