The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL)
The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL)
复制标题
第一届知识转移智能推荐系统研讨会
DOI：
10.1145/3109859.3109951
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Ignacio Fernández
中科院分区：
文献类型：
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作者：
Yong Zheng;Weike Pan;Shaghayegh Sherry Sahebi;Ignacio Fernández
跨领域推荐系统和迁移学习方法有助于整合来自不同地方的知识，从而缓解现有的一些问题（如冷启动问题），或提高推荐系统的质量。凭借这些技术的优势，我们举办了第一个关于知识转移和学习智能推荐系统（RecSysKTL）的国际研讨会，为来自世界各地的学术界研究人员和应用程序开发人员提供这样一个论坛，以展示他们的工作并讨论令人兴奋的研究想法或成果。该研讨会将于8月27日在意大利科莫与2017年ACM推荐系统会议一起举行。
Cross-domain recommender systems and transfer learning approaches are useful to help integrate knowledge from different places, so that we alleviate some existing problems (such as the cold-start problem), or improve the quality of recommender systems. With the advantages of these techniques, we host the first international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for academia researchers and application developers from around the world to present their work and discuss exciting research ideas or outcomes. The workshop is held in conjunction with the ACM Conference on Recommender Systems 2017 on August 27th at Como, Italy.