计算机科学与探索2017，Vol.11Issue(6)：875-886,12.DOI:10.3778/j.issn.1673-9418.1605045
基于四部图的协同过滤推荐算法比较研究
Comparison Study of Collaborative Filtering Algorithms Based on Quadripartite Graph
摘要
Abstract
A recommender system often collects information about user profiles, item attributes and explicit ratings of users to items, which are further used to make predictions about unknown ratings. This paper constructs a quadri-partite graph about the information and acquires ten algorithms from different parts of the graph. The first two algo-rithms are the classical user-and item-based collaborative filtering and only take into account the rating information. Four more algorithms take user or item as center and use relevant tags to compute user or item similarity. To extend the previous four algorithms, four more algorithms take into account the user-item relationship along with tag infor-mation. This paper compares the time complexity of different algorithms on two MovieLens data sets, and uses MAE (mean absolute error) and RMSE (root-mean-square error) metrics to evaluate the performance of different algorithms. The experimental results demonstrate that the similarity of items is more reliable than that of users, and item tags are more useful than user tags. Besides, some simple linear integrations of different information are capa-ble of enhancing recommendation performance.关键词
推荐系统/协同过滤/四部图/协同过滤标签Key words
recommender system/collaborative filtering/quadripartite graph/collaborative filtering tag分类
信息技术与安全科学引用本文复制引用
牟斌皓,张智恒,张林,闵帆..基于四部图的协同过滤推荐算法比较研究[J].计算机科学与探索,2017,11(6):875-886,12.基金项目
The National Natural Science Foundation of China under Grant No. 61379089 (国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Department of Education of Sichuan Province under Grant No. 16ZA0060 (四川省教育厅自然科学基金). （四川省教育厅自然科学基金）
