基于协同过滤的大规模定制个性化推荐方法

为了对用户的产品定制决策进行动态辅助和引导,让用户在大规模定制系统中的产品配置过程变得更简单、方便,在产品定制环节引入了协同过滤推荐算法的思想,并结合大规模定制的特点对推荐算法进行适当改进后提出了一种新的面向大规模定制的个性化推荐算法。给出一个手机定制的实例,对算法产生推荐结果的具体过程进行了模拟与分析。仿真实验产生的可行推荐方案表明,该算法对于解决大规模定制模式下的个性化推荐问题是可行有效的。     

Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network

Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix is constructed by choosing the k-nearest neighbor set of users within the community, in this case, the collaborative filtering algorithm is used for recommendation. Thus, the execution efficiency of the algorithm is improved without reducing the accuracy of recommendation.     

Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency. The proposed algorithm includes two privacy preserving operations: Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation. Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method, meaning that neighbors are privately selected for the target user according to his/her similarities with others. Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation. Our algorithm is evaluated using the real-world hospital EMRs dataset. Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.     

基于用户画像与协同过滤的大规模定制智能推荐算法研究

大规模定制模式的兴起与发展有效缓解了用户对差异化、个性化产品的需求与追求定制化产品成本高昂之间的矛盾。为更高效地辅助用户在大规模定制过程中做出满意的产品定制决策,对传统面向大规模定制的推荐算法进行相应改进,并结合大规模定制的特征,提出基于用户画像的定制方案推荐算法。选用基于物品的协同过滤算法作为基础推荐算法,引入大数据工具——用户画像模型对初始推荐结果进行二次过滤,以改善传统协同过滤推荐算法易忽视用户自身兴趣偏好特征的问题,提高用户定制体验与推荐精准性。给出手机产品定制案例,对产生最终推荐结果的整个过程进行模拟与分析,验证该推荐算法的有效性和可行性。     

Generating A New Shilling Attack for Recommendation Systems

A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing. To keep the recommendation systems reliable, authentic, and superior, the security of these systems is very crucial. Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks, in this paper, we prove that they fail to detect a new or unknown attack. We develop a new attack model, named Obscure attack, with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended. The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list. Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks. The effectivity of the proposed attack model is tested on the MovieLens dataset, where various classifiers like SVM, J48, random forest, and naïve Bayes are utilized.     

面向C2B个性化定制的智能推荐算法研究

发展C2B（消费者到企业）个性化定制是制造企业转型升级的重要方式之一,但当前企业个性化定制水平低,用户参与定制的流程中并未引入智能推荐以辅助其进行定制。为了更好地对用户的产品定制和决策进行引导,使用户可以准确描述自身需求,提高定制效率,在产品的个性化定制中,引入了智能推荐的思想,在原有基于物品的协同过滤推荐算法的基础上进行改进,提出了适用于C2B个性化定制的分步式智能推荐算法,并引入一汽车定制案例。对算法的运算及生成推荐结果的过程进行模拟,证明了该算法的有效性和实用性。