第一届大数据分析情景感知推荐系统国际研讨会(CARS-BDA)

Xiangmin Zhou, Ji Zhang, Yanchun Zhang
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引用次数: 1

摘要

随着网络服务平台的爆炸式增长,越来越多的人和企业在网上做任何事情。为了让组织、政府和个人了解他们的用户,并推广他们的产品或服务,他们有必要实时分析大数据并推荐媒体或在线服务。对于零售、电子商务和在线媒体等领域的企业来说,有效地向消费者推荐感兴趣的产品已经变得至关重要。在商业成功的推动下,这一领域的学术研究也活跃了多年。虽然已经取得了许多科学突破,但在为现实世界的工业应用开发有效和可扩展的推荐系统方面仍然存在巨大的挑战。现有的解决方案侧重于根据预设的上下文(如时间、地点、天气等)推荐商品。大数据规模和复杂的上下文信息给高级推荐系统的部署带来了进一步的挑战。本次研讨会旨在汇集具有广泛背景的研究人员,以确定重要的研究问题,从不同的研究学科交流思想,更广泛地说,促进在上下文感知推荐系统和大数据分析领域的讨论和创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)
With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Through many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.
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