Recommendation system for sharing economy based on multidimensional trust model

Abstract

The recommendation system are widely adopted in today's mainstream online sharing services, providing useful prediction of user's rating or user's preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user's decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user's behavior based on rating analysis and identification of users' historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users' needs, are the focus of this paper.

Defining the Sharing Economy for Sustainability

“…• Improving access to information [113,[115][116][117] Increasing convenience [79,119] The underlying explanatory factor for this increased economic efficiency in our dataset is primarily linked to technological innovation. While offline sharing exists, many sharing organisations utilise some form of technology.…”

Improved Recommendations Based on Trust Relationships in Social Networks

“…The experimental results show that the method has a satisfactory accuracy of recommendation in top-k ranking, but a partial recall due to time complexity. Based on the technology of Tensor Factorization, Kim and Yoon presented a trust model that represented the extra information as a tensor in [7]. The model can integrate multidimensional contextual information to provide satisfactory recommendations, but it lacks flexibility.…”

Sharing Economy Research

“…For IT as an Enabler, reference [18] proposes a framework distinguishing strategies, processes and systems as the three core layers, which may serve to map the different approaches and provide guidance. Then reference [19] recognizes the environment-context as an important factor to consider in personalized recommender systems. As for the intelligent network, reference [20] analyzes the role of the Global Brain in the sharing economy, by synthesizing the notion of distributed intelligence with the concept of an offer network.…”

“…It has been successfully reframed as purely an economic opportunity [5]. Hence, there is a considerable need for exploring how the sharing economy could be steered toward a pathway aligned with a transition to sustainability [6]. And perhaps the most prominent opportunity for Sharing Economy lies in understanding of the role of digital technologies in the dynamics of transitions.…”