解决推荐系统中明显的多样性-准确性困境
Solving the apparent diversity-accuracy dilemma of recommender systems
摘要：
推荐系统使用过去用户偏好的数据来预测未来可能的喜好和兴趣。一个关键的挑战是，虽然最有用的个人推荐是在不同的利基对象中找到的，最可靠的准确结果是通过基于用户或对象相似性推荐对象的方法获得的。在本文中，我们介绍了一种新的算法，专门解决多样性的挑战，并展示了它如何可以用来解决这个明显的困境时，结合在一个优雅的混合与精度为重点的算法。通过适当地调整混合，我们能够获得，而不依赖于任何语义或上下文特定的信息，同时增益的准确性和多样性的建议。
英文摘要：
Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
