Incomplete preference relations to smooth out the cold-start in collaborative Recommender Systems
Incomplete preference relations to smooth out the cold-start in collaborative Recommender Systems
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DOI：
10.1109/nafips.2009.5156454
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
L. Martínez;L. G. Pérez;M. Barranco
中科院分区：
文献类型：
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作者：
L. Martínez;L. G. Pérez;M. Barranco
电子商务公司开发了一些工具，帮助用户找到最适合他们需要或喜好的商品。在这个领域最成功的工具是推荐系统。这种软件获取用户的品味、观点、必需品等信息，并通过推荐算法推断出最适合用户的商品。这些算法通常需要大量的信息，而这些信息并不总是可用或容易获得的。克服这个问题，即所谓的冷启动问题，并减少对信息的需求并不是一件容易的事情。在这篇文章中，我们回顾了这个主题，并提出了我们的建议：一个混合推荐系统，它结合了协作过滤算法和基于知识的算法，以改善冷启动问题。
E-Commerce companies have developed tools to assist users in finding the most suitable items for their needs or preferences. The most successful tool in this area has been the Recommender Systems. This kind of software obtains information about the users' tastes, opinions, necessities, and with a recommendation algorithm, infers recommendations that lead users to the most suitable items for them. These algorithms usually require a significant quantity of information and that information is not always available or easy to obtain. Overcome this problem, known as the cold-start problem, and reduce the requirements of information is not an easy task. In this contribution, we review this topic and present our proposal: a hybrid recommender system which combines a collaborative filtering algorithm with a knowledge-based one in order to improve the cold-start problem.