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To survive in a fiercely competitive business environment, it has become increasingly important for physical retailers to provide customers with services offering a better shopping experience. Many renovate and enlarge their shopping spaces to make their stores more enjoyable places to visit. The growth in social media and the use of mobile devices provide retailers with an opportunity to offer a context-aware guidance service to enhance customers' in-store shopping experience. In this research, by extracting and analysing shopping information (shopping context, visiting trajectory) and social information (user's interest, friends' influence), a contextual store shopping recommendation system is proposed to provide an appropriate route for first-time customers or those who are unfamiliar with a retailer's shopping space. Our experimental results show that the proposed model is effective in providing an appropriate shopping route and enhancing users' shopping experience, which could significantly improve the profitability and competitive advantage of the retailers. Mobile devices provides retailers with an opportunity to offer a context-aware guidance service for in-store shopping.We propose a contextual store shopping recommendation system for customers shopping path support.The proposed framework is developed based on extracting and analyzing shopping information and social information.The proposed model is effective in providing an appropriate shopping route and enhancing users shopping experience.