本文是基于微软Xbox应用,推荐领域的一篇文献. 套用机器学习的思路,它其实是要解决两个分类问题: 1) item 二分类, 2) item list 二分类. item 二分类问题就是典型的隐式反馈推荐系统所要解决的问题; 而item list 二分类则是本文的核心,它关注的是用户会不会点击该list中的任何一个物品. 而本文直接将其作为优化目标,旨在提高系统的CTR(Click Through Rate).
我会从以下几个角度阐述本文的主要思路和工作:
隐式推荐系统
决策树分类器
整体解决思路和实验设置
收获和启发
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Beyond Collaborative Filtering: The List Recommendation Problem | |||||||||||
| 文献作者 | Oren Sar Shalom; Noam Koenigstein | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| Collaborative Filtering, Click prediction; www; GBT | |||||||||||
| 摘要描述 | |||||||||||
| Most Collaborative Filtering (CF) algorithms are optimized using a dataset of isolated user-item tuples. However, in commercial applications recommended items are usually served as an ordered list of several items and not as isolated items. In this setting, inter-item interactions have an effect on the list’s Click-Through Rate (CTR) that is unaccounted for using traditional CF approaches. Most CF approaches also ignore additional important factors like click propensity variation, item fatigue, etc. In this work, we introduce the list recommendation problem. We present useful insights gleaned from user behavior and consumption patterns from a large scale real world recommender system. We then propose a novel two-layered framework that builds upon existing CF algorithms to optimize a list’s click probability. Our approach accounts for inter-item interactions as well as additional information such as item fatigue, trendiness patterns, contextual information etc. Finally, we evaluate our approach using a novel adaptation of Inverse Propensity Scoring (IPS) which facilitates off-policy estimation of our method’s CTR and showcases its effectiveness in real-world settings. | |||||||||||
