In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)++ for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD++, Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100K, MovieLens 1M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation (CV={5,10,15}) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100K dataset (RMSE=0.9123, MAE=0.7149). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.

中文翻译：

---

Rec-CFSVD++：使用协同过滤和奇异值分解 (SVD)++ 实现推荐系统

在推荐系统中，协同过滤（CF）在促进推荐服务方面起着至关重要的作用。传统的 CF 方法存在局限性，即数据稀疏性和冷启动。矩阵分解方法被证明是开发推荐系统中使用的有效方法之一。本文提出了一种使用 CF 和奇异值分解 (SVD) 的新方法++用于实现推荐系统。因此，这项工作试图通过以下方式扩展现有推荐系统：（i）使用余弦相似度从评分矩阵中找到用户和项目之间的相似度；(ii) 使用矩阵分解方法预测缺失的评分，以及 (iii) 推荐前 N 个用户最喜欢的项目。推荐系统的性能评估考虑了均方根误差 (RMSE) 和平均绝对误差 (MAE)。性能评估是通过比较使用 CF 开发的系统并结合六种不同的算法来完成的，即 SVD、SVD++、联合聚类、KNNBasic、KNNBaseline 和 KNNWithMeans。我们已经尝试使用 MovieLens 100K，电影镜头 1M 和 BookCrossing 数据集。结果证明，所提出的方法在交叉验证时给出了较小的错误率（简历={5,10,15}) 被执行。实验结果表明，MovieLens 100 的错误率最低K 数据集 (均方根误差=0.9123,MAE=0.7149）。所提出的方法还缓解了稀疏性和冷启动问题，并推荐了相关项目。