The wisdom of the few
The wisdom of the few
复制标题
少数人的智慧
DOI：
10.1145/1571941.1572033
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Amatriain X
中科院分区：
文献类型：
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作者：
Amatriain X
最近邻协同过滤提供了一个成功的方法来产生建议的Web用户。然而，这种方法有几个缺点，包括数据稀疏性和噪声，冷启动问题和可扩展性。在这项工作中，我们提出了一种新的方法，推荐项目的用户根据专家的意见。我们的方法是传统的协同过滤的变化：而不是应用最近邻算法的用户评级数据，预测计算使用一组专家邻居从一个独立的数据集，其意见是根据他们的相似性加权用户。这种方法有望解决传统协同过滤中的一些弱点，同时保持相当的准确性。我们通过预测Netflix数据集的一个子集来验证我们的方法。我们使用从专家评论门户网站上抓取的评级，从预测准确性和推荐列表准确性两个方面来衡量结果。最后，我们探索我们的方法生成有用的建议的能力，通过报告用户研究的结果，用户更喜欢我们的方法生成的建议。
Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.