A recommendation algorithm using multi-level association rules
Recommendation systems predict user's preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each user's known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.
This paper details the development of a simplified MRP through the equation of Leontief (linear algebra), and proposes its teaching in three parts a) data collection and recognition of the relationship between materials, b) a list of variables and mathematics explanation, c) obtaining results and verifying them by software. The academic exercise is about the flow of ingredients required to comply with a plan for food services and is intended to be a learning tool to facilitate the study of this method in the hospitality management courses.