Exploiting big data for customized online news recommendation system

新闻行业正在经历重大变革，来自非传统国际竞争者的竞争对读者水平（浏览量）和与每次浏览量相关的广告收入都产生了负面影响。《环球邮报》正在寻求一种机器学习和大数据解决方案，以帮助他们在这一变革时期脱颖而出。一个为每个用户提供个性化内容推荐的系统将大有帮助。然而，因为他们的内容库，类似于零售商的产品目录，每分钟都会随着新鲜文章的到来而发生巨大的变化，传统的推荐系统将很难提供好的新鲜文章推荐。传统的推荐系统也没有考虑到受欢迎程度与内容推广程度的关系，也没有考虑到内容带来的商业收益。该项目将结合大数据和先进算法，在推动个性化内容推荐的同时考虑这些因素。

The news industry is undergoing major changes, with competition from non-traditional, international competitors negatively impacting both readership levels (pageviews) and the ad revenue associated to each pageview. The Globe and Mail is seeking a machine learning & big data solution to help them come out on top in this period of change. A system that offers personalized content recommendations to each user would help greatly. However, because their content library, akin to a product catalog at a retailer, changes dramatically every minute with the arrival of fresh news articles, traditional recommender systems would have a very hard time providing good recommendations of fresh articles. Traditional recommender systems also fail to consider popularity as a function of how much a piece of content was promoted, and the business consideration of the revenue driven by a piece of content. This project will combine big data and advanced algorithms to account for these considerations while driving personalized content recommendations.