A novel matrix factorization model for recommendation with LOD-based semantic similarity measure. (1st June 2019)

• Record Type: Journal Article
• Authors: Wang, Ruiqin; Cheng, Hsing Kenneth; Jiang, Yunliang; Lou, Jungang
• Is Part Of: Expert systems with applications. Volume 123(2019)
• Page Start: 70
• Page End: 81
• Publication Date: 2019-06-01
• Subjects: Collaborative filtering -- Matrix factorization -- Implicit feedback -- Semantic similarity -- Linked open data -- DBpedia; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.01.036
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A MF model uses implicit feedback and semantic similarity to extend user & item vector. A novel semantic similarity measure which uses feature- and distance-based metrics. A general recommender framework. Based on it, other knowledge bases can be used. Abstract: Collaborative Filtering (CF) algorithms have been widely used to provide personalized recommendations in e-commerce websites and social network applications. Among them, Matrix Factorization (MF) is one of the most popular and efficient techniques. However, most MF-based recommender models only rely on the past transaction information of users, so there is inevitably a data sparsity problem. In this article, we propose a novel recommender model based on matrix factorization and semantic similarity measure. Firstly, we propose a new semantic similarity measure based on semantic information in the Linked Open Data (LOD) knowledge base, which is a hybrid measure based on feature and distance metrics. Then, we make an improvement on the traditional MF model to deal with data sparsity. Specifically, the MF process has been extended from both the user and item sides with implicit feedback information and semantic similar items, respectively. Experiments on two real datasets show that our proposed semantic similarity measure and recommender model are superior to the state-of-the-art approaches in recommendation performance.