ISSN 1000-1239 CN 11-1777/TP

• 信息处理 •

### 一种播存网络环境下的UCL协同过滤推荐方法

1. (东南大学计算机科学与工程学院 南京 211189) (计算机网络和信息集成教育部重点实验室(东南大学) 南京 211189) (guliang@seu.edu.cn)
• 出版日期: 2015-02-01
• 基金资助:
基金项目：国家“八六三”高技术研究发展计划基金项目(2013AA013503)；国家自然科学基金项目(61472080);中国工程院咨询研究项目(2013-XY-6)

### A Collaborative Filtering Recommendation Method for UCL in Broadcast-Storage Network

Gu Liang, Yang Peng, Luo Junzhou

1. (School of Computer Science and Engineering, Southeast University, Nanjing 211189) (Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189)
• Online: 2015-02-01

Abstract: Problems like bandwidth congestion, content redundancy exist in the sharing of information resources. Broadcast-Storage network has a particular advantage in solving these issues because of its unique feature of one to infinite by physical broadcast. Uniform content label, UCL, is used to express the needs of users and help users understand the information resources in Broadcast-Storage environment. Due to UCL’s large quantity, how to guide users to get their preferred UCLs efficiently is quite significant. To address this problem, this paper proposes a unifying collaborative filtering method with popularity and timing (UCF-PT) for UCL recommendation. First, a pair of thresholds are set to estimate the sparsity of users and UCLs in the dataset and determine the weights of users and UCLs in recommendation. Then UCF-PT predicts the ratings of users on UCLs based on the weights and generates a recommendation list. Moreover, the method makes popular and new UCLs more likely to be recommended by considering UCL popularity and using exponential decay in recommendation. Experiments show that, compared with traditional recommendation methods, the method proposed in this paper possesses better recommendation accuracy and ensures the popularity and novelty of recommended UCLs. Therefore, it is more suitable for recommending UCLs in Broadcast-Storage environment.