Personalized Collaborative Clustering

被引:10
作者
Yue, Yisong [1 ]
Wang, Chong [2 ]
El-Arini, Khalid [3 ]
Guestrin, Carlos [4 ]
机构
[1] Disney Res, Los Angeles, CA 91201 USA
[2] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[3] Facebook, Menlo Pk, CA USA
[4] Univ Washington, Seattle, WA 98195 USA
来源
WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB | 2014年
关键词
Personalization; Clustering; Tensor Factorization;
D O I
10.1145/2566486.2567991
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.
引用
收藏
页码:75 / 84
页数:10
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