The growth of the number of e-commerce users and the items being sold become both opportunities and challenges for e-commerce marketplaces. As the existence of the long-tail phenomenon, the marketplaces need to pay attention to the high number of rarely sold items. The failure to sell these products would be a threat for some B2C e-commerce companies that apply a non-consignment sale system because the products cannot be returned to the manufacturer. Thus, it is important for the marketplace to boost the promotion of long-tail products. The objective of this study is to adapt the graph-based technique to build the recommendation system for long-tail products. The set of products, customers, and categories are represented as nodes in the tripartite graph. The Absorbing Time and Hitting Time algorithms are employed together with the Markov Random Walker to traverse the nodes in the graph. We find that using Absorbing Time achieves better accuracy than the Hitting Time for recommending long-tail products.

Competition in the culinary business is very fast as it is currently encouraging the development of information technology to provide convenience services for consumers. Information technology is needed by all types of companies, both for-profit and non-profit companies. In the field of consumer service to attract more interest in buying, information technology is used to help facilitate transactions, especially in terms of ordering. The purpose of this research is to design an application that can be used in the process of ordering transactions and executing queues for ordering culinary products. The application is designed using Android Studio with SQLServer database. The output obtained includes an android-based application that is used independently by consumers.