Use of social network information to enhance collaborative filtering performance
Use of social network information to enhance collaborative filtering performance
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DOI：
10.1016/j.eswa.2009.12.061
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发表时间：
2010-07-01
影响因子：
8.5
通讯作者：
Lee, Hong Joo
中科院分区：
文献类型：
--
作者：
Liu, Fengkun;Lee, Hong Joo
当人们做出决定时，他们通常依赖朋友和熟人的推荐。尽管协同过滤（CF）是最流行的推荐技术，利用相似的邻居来生成推荐，但它并不能区分邻居中的朋友和具有相似品味的陌生人。由于社交网络网站现在可以轻松收集社交网络信息，因此有关使用社交网络信息进行推荐的研究可能会产生富有成效的结果。在本研究中，我们开发了一种通过将社交网络信息合并到 CF 中来提高推荐效果的方法。我们从社交网站收集了有关用户偏好评分及其社交网络关系的数据。然后，我们结合朋友组和最近邻居组来评估不同邻居组的 CF 性能。我们的结果表明，通过将社交网络信息合并到 CF 中可以产生更准确的预测算法。 (C) 2009 Elsevier Ltd. 保留所有权利。
When people make decisions, they usually rely on recommendations from friends and acquaintances. Although collaborative filtering (CF), the most popular recommendation technique, utilizes similar neighbors to generate recommendations, it does not distinguish friends in a neighborhood from strangers who have similar tastes. Because social networking Web sites now make it easy to gather social network information, a study about the use of social network information in making recommendations will probably produce productive results.In this study, we developed a way to increase recommendation effectiveness by incorporating social network information into CF. We collected data about users' preference ratings and their social network relationships from a social networking Web site. Then, we evaluated CF performance with diverse neighbor groups combining groups of friends and nearest neighbors. Our results indicated that more accurate prediction algorithms can be produced by incorporating social network information into CF. (C) 2009 Elsevier Ltd. All rights reserved.