Recommender System for Music CDs Using a Graph Partitioning Method
Recommender System for Music CDs Using a Graph Partitioning Method
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DOI：
10.1007/978-3-642-04592-9_33
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Takanobu Nakahara;H. Morita
中科院分区：
文献类型：
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作者：
Takanobu Nakahara;H. Morita
协同过滤用于推荐系统中用户偏好的预测，例如用于推荐电影、音乐或文章。这种方法对公司的业务有很好的效果。亚马逊和Netflix等电子商务公司已经成功地使用推荐系统来增加销售额和提高客户忠诚度。然而，这些系统通常需要对电影、音乐等进行评级。获得这种评级数据与交易数据的比较通常是困难的或昂贵的。因此，我们需要一个高质量的推荐系统，它只使用历史购买数据而不使用评级。本文讨论了基于图划分方法的推荐系统的有效性。在数值计算实验中，我们将我们的方法应用于CD的购买数据，并将我们的结果与传统方法得到的结果进行了比较。这表明我们的方法更适合于商业。
Collaborative filtering is used for the prediction of user preferences in recommender systems, such as for recommending movies, music, or articles. This method has a good effect on a company’s business. E-commerce companies such as Amazon and Netflix have successfully used recommender systems to increase sales and improve customer loyalty. However, these systems generally require ratings for the movies, music, etc. It is usually difficult or expensive to obtain such ratings data comparison with transaction data. Therefore, we need a high quality recommender system that uses only historical purchasing data without ratings. This paper discusses the effectiveness of a graph-partitioning method based recommender system. In numerical computational experiments, we applied our method to the purchasing data for CDs, and compared our results with those obtained by a traditional method. This showed that our method is more practical for business.