The 2nd workshop on intelligent recommender systems by knowledge transfer & learning (recsysKTL)
The 2nd workshop on intelligent recommender systems by knowledge transfer & learning (recsysKTL)
复制标题
第二届知识转移智能推荐系统研讨会
DOI：
10.1145/3240323.3240339
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发表时间：
2018
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：
Shaghayegh Sherry Sahebi;Yong Zheng;Weike Pan;Ignacio Fernández
拥有多来源的数据，跨领域和上下文感知的推荐系统，在迁移学习方法的帮助下，旨在整合这些数据以提高推荐质量，缓解冷启动问题等问题。凭借这些技术的优势，我们主办了第二届知识转移和学习智能推荐系统国际研讨会（RecSysKTL），为来自世界各地的学术界和行业研究人员以及应用程序开发人员提供了这样一个论坛，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2018年10月6日在加拿大温哥华与ACM推荐系统会议同时举行。
Having data from multiple sources, cross-domain and context-aware recommender systems, with the help of transfer learning approaches, aim to integrate such data to improve recommendation quality and alleviate issues such as cold-start problem. With the advantages of these techniques, we host the second international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for both academia and industry researchers as well as 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 2018 on October 6th in Vancouver, Canada.