基于k-DNF规则集的基于决策表分类的基于内容的推荐系统

Abinash Pujahari, V. Padmanabhan
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引用次数: 9

摘要

推荐系统是一种软件或技术工具,它可以帮助用户从大量的物品/事物中根据他/她的喜好找到物品/事物。例如,从在线电影的大型数据库中选择一部电影,或者从互联网上大量可用的歌曲中选择一首自己的歌曲等等。为了为用户生成推荐,系统必须首先从用户过去的行为中了解用户的偏好,以便它可以预测适合各自用户的新项目/事物。这些系统通常从用户过去的经验中学习用户的偏好,使用任何机器学习算法,并使用学习到的偏好为用户预测新的项目/事物。在本文中,我们介绍了一种不同的推荐系统方法,该方法将使用基于决策列表的分类来学习用户偏好规则。我们遵循了两种基于决策列表的分类算法,如重复增量修剪以产生错误减少和预测规则挖掘,用于学习用户过去行为的规则。我们还列出了我们提出的推荐算法,并讨论了我们的推荐系统与传统方法的优缺点。我们用电影镜头数据集验证了我们的推荐系统,该数据集包含来自不同用户的十万部电影评分,这是推荐系统测试的基准数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Approach to Content Based Recommender Systems Using Decision List Based Classification with k-DNF Rule Set
Recommender systems are the software or technical tools that help user to find out items/things according to his/her preferences from a wide range of items/things. For example, selecting a movie from a large database of movies from on-line or selecting a song of his/her own kind from a large number of songs available in the internet and much more. In order to generate recommendations for the users the system has to first learn the user preferences from the user's past behaviours so that it can predict new items/things that are suitable for the respective user. These systems generally learn user's preferences from user's past experiences, using any machine learning algorithm and predict new items/things for the user using the learned preferences. In this paper we introduce a different approach to recommender system which will learn rules for user preferences using classification based on Decision Lists. We have followed two Decision List based classification algorithms like Repeated Incremental Pruning to Produce Error Reduction and Predictive Rule Mining, for learning rules for users past behaviours. We also list out our proposed recommendation algorithm and discuss the advantages as well as disadvantages of our approach to recommender system with the traditional approaches. We have validated our recommender system with the movie lens data set that contains hundred thousand movie ratings from different users, which is the bench mark dataset for recommender system testing.
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