The recommender system’s primary purpose is to estimate the user’s desire and provide a list of items predicted from the appropriate information. Also, context-aware recommendation systems are becoming more and more favorite since they could provide more accurate or personalized recommendation information than traditional recommendation techniques. However, a context-aware recommendation system suffers from two fundamental limitations known as cold start and sparse data. Singular value decomposition has been successfully integrated with some traditional recommendation algorithms. However, the basic singular value decomposition can only extract the feature vectors of users and items, resulting in lower recommendation precision. To improve the recommendation performance and reduce the challenge of cold start and sparse data, we propose a new context-aware recommendation algorithm, named CSSVD. First, in the CSSVD matrix, using the IFPCC and DPCC similarity criteria, the item’s user property attribute matrices are created, respectively, creating the SSVD matrix for the cold start problem. In the second step, through the CWP similarity criterion on the contextual information, the context matrix is created, which according to the SSVD matrix created in the previous step, creates a three-dimensional matrix based on tensor properties, providing the problem of sparse data. We have used the IMDB and STS data collection because of implementing user features, item features, and contextual data for analyzing the recommended method. Experiential results illustrate that the proposed algorithm CSSVD is better than TF, HOSVD, BPR, and CTLSVD in terms of Precision, Recall, F-score, and NDCG measure.Results show the improvement of the recommendations to users through alleviating cold start and sparse data.

推荐系统的主要目的是估计用户的需求并提供从适当信息中预测的项目列表。而且，情境感知推荐系统变得越来越受欢迎，因为它们可以提供比传统推荐技术更准确或个性化的推荐信息。但是，上下文感知推荐系统遭受两个基本限制，即冷启动和稀疏数据。奇异值分解已成功与某些传统推荐算法集成在一起。但是，基本奇异值分解只能提取用户和商品的特征向量，推荐精度较低。为了提高推荐效果并减少冷启动和稀疏数据的挑战，我们提出了一种新的上下文感知推荐算法CSSVD。首先，在CSSVD矩阵中，使用IFPCC和DPCC相似性标准，分别创建商品的用户属性属性矩阵，从而为冷启动问题创建SSVD矩阵。第二步，根据上下文信息的CWP相似性标准，创建上下文矩阵，该上下文矩阵根据上一步中创建的SSVD矩阵，基于张量属性创建三维矩阵，从而提供数据稀疏的问题。我们之所以使用IMDB和STS数据收集，是因为实现了用户功能，项目功能和上下文数据来分析推荐的方法。实验结果表明，在精度，召回率，F得分，