Adjusting data sparsity problem using linear algebra and machine learning algorithm

• Published in: Applied soft computing Vol. 61; pp. 1153 - 1159
• Main Authors: Nasiri, Mahdi, Minaei, Behrouz, Sharifi, Zeinab
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2017
• Subjects: Collaborative filtering; Data sparsity; Spectral co-clustering; Tensor factorization; Collaborative filtering; Data sparsity; Tensor factorization; Spectral co-clustering
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.05.042

[Display omitted] •Propose a new algorithm for recommender systems based on linear algebra and machine learning algorithms.•Proposed algorithm can recommend items to users in the special time.•The proposed algorithm is able to obtain better results for both dynamic and classic recommendation.•Spectral co-clustering algorithm use for data sparsity and also more similarity among users and items. Data sparsity is one of the most important challenges in data in which each user only rates a small set of items. This problem is critical with increasing dimensions of data. We present an idea based on linear algebra and machine learning to solve this problem. This research applies a framework to cluster users and items in similar groups simultaneously. This method imputes appropriate values for missing data based on similar ratings in each cluster. This has the advantages of more accurate process results in each cluster due to users’ similarity of interests, and the reduction of sparsity negative effect. This approach is represented on 3dimensional data of users, items and times. The experimental results on MovieLense datasets, show that the method can help to overcome data sparsity, and increase the accuracy of prediction.