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英文文献：Challenges And Solutions To Providing Responsible Recommendations In Social Media-在社交媒体上提供负责任的建议的挑战和解决方案
英文文献作者：?erban-Ionu? Georgescu,Maria-Iuliana Dasc?lu
英文文献摘要：
Recommender systems (RS) are a type of information filtering software tools and techniques with the purpose of predicting the rating or affinity of a user to an item (such as music, books, movies) they had not yet considered, using the characteristics of an item (content based approaches) or the user’s social environment (collaborative filtering based approaches). In recent years, RS have become very common in e-commerce, social media and the Internet in general, being an important part of many users’ personalized browsing experiences. Accordingly, there is a need for generating socially responsible recommendations. The immediate objective of this work is to present ways of generating such recommendations in the context of social media and, to a lesser extent, e-commerce. As a research methodology, a critical survey was made, taking into account several major directions, such as: how the relevance of recommendations can be increased, how privacy-related issues that affect user data based recommender systems can be solved or how malicious recommendations can be eradicated. This work is part of a growing body of work on RS research, which aims at improving the reliability factor of e-recommendations and, implicitly, adding value to various RS stakeholders (users and providers).
推荐系统(RS)是一种信息过滤软件工具和技术的目的和预测用户的等级或关联一个项目(如音乐、书籍、电影),他们还没有考虑使用一个项目的特点(基于内容的方法)或用户的社会环境(基于协同过滤的方法)。近年来，RS在电子商务、社交媒体和一般互联网中非常普遍，成为许多用户个性化浏览体验的重要组成部分。因此，有必要提出对社会负责的建议。这项工作的直接目标是提出在社交媒体和电子商务环境下产生此类推荐的方法。作为研究方法,至关重要的一项调查,考虑几个主要方向,如:如何提高推荐的相关性,基于用户数据隐私相关问题如何影响推荐系统可以解决或如何根除恶意推荐。这项工作是越来越多的RS研究工作的一部分，其目的是提高电子推荐的可靠性，并暗中增加RS的利益相关者(用户和提供者)的价值。
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