利用已有的用户群过去的行为或者意见预测当前用户最可能喜欢哪些东西或者对哪些东西感兴趣。主要应用场景是在线零售系统,目的是进行商品促销和提高销售额。

VIP内容

题目

保护隐私的协同过滤综述,Survey of Privacy-Preserving Collaborative Filtering

关键字

协同过滤,隐私保护,机器学习,人工智能,推荐系统

简介

协作过滤推荐系统根据用户过去的经验以及具有相似兴趣的其他用户的经验向用户提供建议。推荐系统的使用在最近几年得到了广泛的发展,可以帮助人们选择观看哪些电影,阅读哪些书籍以及购买哪些物品。但是,在使用此类系统时,用户通常会担心其隐私,并且许多用户不愿意为大多数在线服务提供准确的信息。隐私保护协作过滤推荐系统旨在为用户提供准确的推荐,同时保持有关其数据隐私的某些保证。这项调查研究了有关保护隐私的协作过滤的最新文献,提供了一个广阔的视野,并使用两种不同的标准对文献中的关键贡献进行了分类:漏洞的类型和解决方法。

作者

Islam Elnabarawy,Student Member, IEEE,Wei Jiang,Member, IEEE,and Donald C. Wunsch II,Fellow, IEEE

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最新论文

Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system. For instance, in a news aggregator website users, authors, magazines as well as the platform itself are potential stakeholders. Most of the collaborative filtering recommender systems suffer from popularity bias. Therefore, if the recommender system only considers users' preferences, presumably it over-represents popular providers and under-represents less popular providers. To address this issue one should consider other stakeholders in the generated ranked lists. In this paper we demonstrate that hypergraph learning has the natural capability of handling a multi-stakeholder recommendation task. A hypergraph can model high order relations between different types of objects and therefore is naturally inclined to generate recommendation lists considering multiple stakeholders. We form the recommendations in time-wise rounds and learn to adapt the weights of stakeholders to increase the coverage of low-covered stakeholders over time. The results show that the proposed approach counters popularity bias and produces fairer recommendations with respect to authors in two news datasets, at a low cost in precision.

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