Discovery-oriented collaborative filtering for improving user satisfaction
Discovery-oriented collaborative filtering for improving user satisfaction
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DOI：
10.1145/1502650.1502663
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发表时间：
2009-02
期刊：
影响因子：
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通讯作者：
Y. Hijikata;Takuya Shimizu;S. Nishida
中科院分区：
文献类型：
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作者：
Y. Hijikata;Takuya Shimizu;S. Nishida
商业网站中使用的许多推荐系统都使用协作过滤。传统的协同过滤技术的主要目标是提高推荐的准确性。然而，这种技术存在一个问题，即它们包括许多用户已经知道的项目。当我们单独考虑准确性时，这些建议似乎是好的。另一方面，当我们考虑用户满意度时，他们不一定是好的，因为缺乏发现。在我们的工作中，我们通过计算用户或项目的相似度来推断用户不知道的项目，或者基于用户已经知道的项目的信息。我们试图通过结合上述方法和最流行的协作过滤方法来推荐用户可能喜欢和不知道的项目。
Many recommender systems employed in commercial web sites use collaborative filtering. The main goal of traditional collaborative filtering techniques is improvement of the accuracy of recommendation. Nevertheless, such techniques present the problem that they include many items that the user already knows. These recommendations appear to be good when we consider accuracy alone. On the other hand, when we consider users' satisfaction, they are not necessarily good because of the lack of discovery. In our work, we infer items that a user does not know by calculating the similarity of users or items based on information about what items users already know. We seek to recommend items that the user would probably like and does not know by combining the above method and the most popular method of collaborative filtering.