Serendipitous Personalized Ranking for Top-N Recommendation
Serendipitous Personalized Ranking for Top-N Recommendation
复制标题
DOI：
10.1109/wi-iat.2012.135
复制
发表时间：
2012-12
期刊：
影响因子：
--
通讯作者：
Qiuxia Lu;Tianqi Chen;Weinan Zhang;Diyi Yang;Yong Yu
中科院分区：
文献类型：
--
作者：
Qiuxia Lu;Tianqi Chen;Weinan Zhang;Diyi Yang;Yong Yu
偶然的推荐使电子零售商和用户都受益。它倾向于建议对用户来说既意想不到又有用的项目。这些商品不仅对零售商有利可图，而且出乎意料地适合消费者的口味。然而，由于流行和尾部项目的观察数据的不平衡，现有的协同过滤方法无法给出令人满意的偶然推荐。为了解决这个问题，我们提出了一个简单而有效的方法，称为偶然的个性化排名。实验结果表明，我们的方法显着提高了准确性和偶然性的前N推荐相比，传统的个性化排名方法在各种设置。
Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. However, due to the imbalance in observed data for popular and tail items, existing collaborative filtering methods fail to give satisfactory serendipitous recommendations. To solve this problem, we propose a simple and effective method, called serendipitous personalized ranking. The experimental results demonstrate that our method significantly improves both accuracy and serendipity for top-N recommendation compared to traditional personalized ranking methods in various settings.