Details

Autor(en) / Beteiligte

• Natarajan, Senthilselvan
• Vairavasundaram, Subramaniyaswamy
• Natarajan, Sivaramakrishnan
• Gandomi, Amir H.

Titel

Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data

Ist Teil von

• Expert systems with applications, 2020-07, Vol.149, p.113248, Article 113248

Ort / Verlag

New York: Elsevier Ltd

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •A MF model with Linked Open Data is developed to handle data sparsity issue.•Hidden data and LOD similarity measure are integrated to enhance recommendations.•The proposed framework can be applied to any domain for recommendations.•Experiments were done on Netflix and Movie Lens datasets for validation. The web contains a huge volume of data, and it's populating every moment to the point that human beings cannot deal with the vast amount of data manually or via traditional tools. Hence an advanced tool is required to filter such massive data and mine the valuable information. Recommender systems are among the most excellent tools for such a purpose in which collaborative filtering is widely used. Collaborative filtering (CF) has been extensively utilized to offer personalized recommendations in electronic business and social network websites. In that, matrix factorization is an efficient technique; however, it depends on past transactions of the users. Hence, there will be a data sparsity problem. Another issue with the collaborative filtering method is the cold start issue, which is due to the deficient information about new entities. A novel method is proposed to overcome the data sparsity and the cold start problem in CF. For cold start issue, Recommender System with Linked Open Data (RS-LOD) model is designed and for data sparsity problem, Matrix Factorization model with Linked Open Data is developed (MF-LOD). A LOD knowledge base “DBpedia” is used to find enough information about new entities for a cold start issue, and an improvement is made on the matrix factorization model to handle data sparsity. Experiments were done on Netflix and MovieLens datasets show that our proposed techniques are superior to other existing methods, which mean recommendation accuracy is improved.