A novel tourism recommender system in the context of social commerce

• Published in: Expert systems with applications 2020-07, Vol.149, p.113301, Article 113301
• Main Authors: Esmaeili, Leila, Mardani, Shahla, Golpayegani, Seyyed Alireza Hashemi, Madar, Zeinab Zanganeh
• Format: Article
• Language: English
• Subjects: Community; Context; Data analysis; Electronic commerce; Hotels; Hybrid systems; Outliers (statistics); Recommender systems; Reputation; Similarity; Social commerce; Social networks; Social relationships; Tourism; Tourism recommender system; Tourist attractions; Trust
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113301

•Proposing a tourism recommender system in the context of social commerce.•Applying of the network analysis metrics and methods beside social networks data.•Employing the customer's reviews as a data source.•Ensuring a more efficient approach based on the f-measure.•Proposing an efficient synergy of trust, similarity, reputation, and social relationships. Web 2.0 and its services, such as social networks, have significantly influenced various businesses, including e-commerce. As a result, we face a new generation of e-commerce called Social Commerce. On the other hand, in the tourism industry, a variety of services and products are provided. The dramatic rise in the number of options in travel packages, hotels, tourist attractions, etc. put users in a difficult situation to find what they need. For a reason, tourism recommender systems have been considered by researchers and businesses as a solution. Since tourist attractions are often the reason for travelling, this research proposes a social-hybrid recommender system in the context of social commerce that recommends tourist attractions. The purpose of the research is presenting a personalized list of tourist attractions for each tourist based on the similarity of users' desires and interests, trust, reputation, relationships, and social communities. Compared with the traditional methods, collaborative filtering, content-based, and hybrid, the advantage of the proposed method is the use of various factors and the inclusion of trust factors in recommendation resources, (such as outlier detection in user ratings), and employing social relationships among individuals. The experimental results show the superiority of the proposed method over other common methods. The proposed method can also be used to recommend other products and services in the tourism industry and other social commerce.