Research on Dining Windows Recommendation AIgorithm for Students in Higher Education Based on Mahout
Constructs an experimental system based on the deep discussion on development process of Mahout recommended system and empirical researches on scoring all dining windows by students. Furthermore, conducts a deep analysis on experimental data through API provided by Mahout recommended system engine. This experimental sample completely narrates the process to create its own recommended engine constructor. The commonly-used collaborative filtering recommendation algorithm is adopted by this experiment which includes collabora-tive filtering of users, collaborative filtering of articles and recommendation algorithm of SlopeOne. Under construction of its own develop-ment environment, adopts seven different combinations to conduct contrast experiment based on similarity algorithm model and recom-mended algorithm. Precision ratio and recall rate are applied to evaluate the seven algorithm combinations. Compares these recommended results through Euclidean distance similarity of users, collaborative filtering of users, collaborative filtering of articles, scoring method and non-scoring method. From the experimental results, it can be concluded that recommended system of Mahout can efficiently and rapidly recommend similar dining windows to students.
NETL
NSTL
万方数据
维普
