图 书 馆
重新审视推荐人问题The Recommender Problem Revisited |
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| 课程网址: | http://videolectures.net/kdd2014_amatriain_mobasher_recommender_... |
| 主讲教师: | Xavier Amatriain; Bamshad Mobasher |
| 开课单位: | 奈飞公司 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  2006年，Netflix宣布了一项100万美元的奖金竞赛，以推进推荐算法。由于通过均方根误差测量的预测用户评分的准确性，简化了推荐问题。尽管该提法引起了该领域研究界的关注，但它可能过度关注了仅仅是建议的一种可能方法。在本教程中，我们将描述现代推荐器系统的不同组件，例如：个性化排名，相似性，解释，上下文意识或搜索作为推荐。我们将使用Netflix用例作为典型的工业规模推荐系统的驱动示例。我们还将回顾现代算法方法的用法，其中包括诸如分解机，受限玻尔兹曼机，SimRank，深度神经网络或Listwise Learningtorank等算法。 |
| 课程简介: | In 2006, Netflix announced a $1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community in the area, it may have put an excessive focus on what is simply one of possible approaches to recommendations. In this tutorial we will describe different components of modern recommender systems such as: personalized ranking, similarity, explanations, context awareness, or search as recommendation. We will use the Netflix use case as a driving example of a prototypical industrial scale recommender system. We will also review the usage of modern algorithmic approaches that include algorithms such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or Listwise Learningtorank. |
| 关 键 词: | 数据挖掘; 知识提取; 推进推荐算法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-06-11：吴淑曼 |
| 最后编审: | 2020-12-18：yumf |
| 阅读次数: | 57 |