We explored the use of blockchain technology for traceability to improve the safety and value of food, focusing on the coffee supply chain as a case study. The main goal was to evaluate the feasibility in terms of design, perceived benefits, and challenges of applying blockchain and traceability from the users' perspective. We implemented a prototype using a user-centered iterative interface design. Then we used the prototype to answer our research questions in mixed-method research, including in-depth interviews (10 participants) and a survey (350 participants) with stakeholders in the coffee supply chain in Thailand. The results showed that timeline-based design was preferred over map-based or text-based design for the visualization of traceability information and that blockchain was a promising technology, as 67% of the survey participants saw a positive influence of blockchain on the adoption of applications. The most notable benefits were origin checking and increasing product trustworthiness. The most notable challenges were inaccurate or incomplete information and the disclosure of trade secrets. More work is required to address the challenges for everyone in the supply chain ecosystem to adopt the proposed traceability system, including (1) providing trustworthiness and completeness of information by cross-checking with third parties or other users, (2) protecting sensitive information by aligning users' interests or allowing control of information disclosure, and (3) educating and giving producers the motivation for the difficulty and the extra work.

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A recommendation system is an information retrieval system that employs user, product, and other related information to infer relationships among data to offer product recommendations. The basic assumption is that friends or users with similar behavior will have similar interests. The large number of products available today makes it impossible for any user to explore all of them and increases the importance of recommendation systems. However, a recommendation system normally requires comprehensive data relating users and products. Insufficiently comprehensive data creates difficulties for creating good recommendations. Recommendation systems for incomplete data have become an active research area. One approach to solve this problem is to use random walk with restart (RWR), which significantly reduces the quantity of data required and has been shown to outperform collaborative filtering, the currently popular approach. This study explores how to increase the efficiency of the RWR approach. We replace transition matrices that use information regarding relationships between user, usage, and tags with transition matrices that use Bayesian probabilities, and we compare the efficiency of the two approaches using mean average precision. An experiment was conducted using music information data from last.fm. The result shows that our approach provides better recommendations.