图 书 馆
个性化搜索和推荐系统的深度学习Deep Learning for Personalized Search and Recommender Systems |
|
| 课程网址: | http://videolectures.net/kdd2017_tutorial8_deep_learning/ |
| 主讲教师: | Liang Zhang; Benjamin Le |
| 开课单位: | 领英公司 |
| 开课时间: | 2017-11-21 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production. Link to tutorial: |
| 关 键 词: | 深度学习; 图像识别; 机器翻译; 个性推荐 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-24：chenxin01 |
| 最后编审: | 2023-05-22：chenxin01 |
| 阅读次数: | 19 |