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2003-2020 全部
为您找到包含“推荐系统”的内容共144

LI Xinsheng,LI Jian

The combination of the recommender system and dialogue system which called the conversational recommendation system is a growing interest. Tosolve the problem that it is difficult to obtain users' tastes in conversational recommendation systems. A sentiment analysis method is proposed in our conversational recommendation model to get user preferences. A sentiment analysis dataset is created and the model uses a sentiment analysis approach to obtain a movie seeker\'s preferences and make a recommendation. Experimentresults show that our sentiment analysis model yields a better performance of 0.8362(F1 score) than the baseline(0.7802) and other models. Thus, the movie recommended by our system can meet the needs of users better.

2020-03-03

Beijing University of Posts and Telecommunications, Beijing, 100876,Beijing University of Posts and Telecommunications, Beijing, 100876

#Computer Science and Technology#

邱飞,杨鹏,陈国庆

2012-04-09

本文在对传统推荐算法进行比较研究的基础上,提出了一种基于云计算的推荐系统(CCBRS),该系统能根据不同的推荐需求采用不同的推荐策略。结合CCBRS系统对单机环境、伪分布式平台以及分布式平台下多种

东南大学软件学院(苏州), 江苏苏州, 215123,东南大学计算机网络和信息集成教育部重点实验室, 江苏南京, 210096,东南大学软件学院(苏州), 江苏苏州, 215123

#计算机科学技术#

本文收录在中国科技论文在线精品论文,2012,5(20):1982-1988.

LI Ye,ZHANG Hua

Recommender Systems aim to predict the rating or preference of a user given to an item and provide suggestions of further resources that are likely to be of interest. However, a lot of information about users need to be acquired for better recommendation result. Those information will leak users\' own privacy, which lead to lose the users\' trust of recommender systems. In recent years, more and more study focused on the various kinds of privacy protection techniques. Our paper proposes a novel method of the privacy preserving on recommender system for the first time. Employ Attribute-Based Encryption technique to protect users\' privacy. Based on the scheme, our paper builds up a recommender system that allows users\' information collection can be controlled by themselves, and those information will be encrypted before sending to the recommendation server so that user\'s privacy can be safeguarded from attacker\'s tracing even if the communication channel is not secure.. Besides, our paper adjusts ABE algorithms to make this technique suitable and efficient for recommender systems. Furthermore, our paper presents the security analysis for our scheme.

2017-12-06

NSFC(Grant Nos. 61502044

Fundamental Research Funds for the Central Universities(No. 2015RC23

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China,

#Computer Science and Technology#

Mingming Wang,Honggang Zhang,Yang Yang,Siyuan Li

Recommender systems are gaining a great importance with the emergence of E-commerce and business on the Internet. These recommender systems help users make decision by suggesting products and services that satisfy the users' tastes and preferences. As an important tool for Internet information exchange, e-mail plays a pivotal role in people's lives. Personalized recommendation in email platform becomes a new hot service. This paper presents a recommender system based on email platform. Our method makes a recommendation through extracting and analyzing user email information and user behavior logs. For every user, we will build a user model according to the user's email information, generating user interest matrix. The user interest matrix records the user's interests to things, including original interests and potential interests. After getting user interest matrix, we can recommend things to the user. Considering the drifting of user interest and the real-time of recommendation, we propose feedback recommendation. When a user clicks one thing, the system will get similar things and then recommend to the user. The similarity between things is stored in thing similarity matrix, which was calculated by content-based and collaborative filtering techniques. Besides, according to user email information and user behavior logs, we change the user interest matrix every once in a while tracking the change of user interests. What's more, as user interest matrix records user's potential interests, the recommendation results are diversity and novelty and the method gives a good recommendation for email platform.

2014-11-03

Beijing University of Posts and Telecommunications, 100876,Beijing University of Posts and Telecommunications, 100876,Beijing University of Posts and Telecommunications, 100876,Beijing University of Posts and Telecommunications, 100876

#Computer Science and Technology#

丁阳,王洪波,程时端

2012-11-21

目前电子商务推荐系统在理论和实践中都得到了很大发展,但现有的推荐系统普遍存在着用户评分稀疏性问题以及用户内在评分标准偏差问题,难以有效建立用户兴趣模型。本文提出了一种基于隐性用户评分的自适应推荐算法

高等学校博士学科点专项科研基金(200800131019

北京邮电大学网络与交换技术国家重点实验室,北京 100876,北京邮电大学 网络与交换技术国家重点实验室,北京邮电大学 网络与交换技术国家重点实验室

#计算机科学技术#