AI Case Study
Washington Post helps improve click-through rate by 95% on its story recommendations using machine learning
The Washington Post utilises a machine learning content recommendation system, Clavis, to drive up page views through its 'Post Recommends' feature. The system shows readers content relevant to their interests and reading histories and has contributed to an increase of 95% on Post Recommends click-through rate.
Consumer Goods And Services
Media And Publishing
"Clavis is technology that figures out what stories are about, categorizes them by topic, and assigns each a series of keywords. It runs that same process on the Washington Post’s readers and identifies their presumed interests based on stories they’ve read. Clavis then pairs readers with stories that match their reading history. The technology also classifies readers in a similar fashion, assigning each reader topics and a group of keywords based on the stories they read. The site’s registered users are obviously easier to assign topics to, but the Washington Post can recommend relevant content to readers who only visit occasionally by using cookies... While the Post and a few other publications have had some success analyzing data and recommending content, it’s a difficult thing for most organizations to do. The Post has an entire data and personalization team and content recommendation is one of its primary projects".
"Clavis is just one part of the larger Post Recommends system and has helped the Post improve its click through rate on Post Recommends 95 percent since last June ".
"Like other content-based recommenders, Clavis maps articles by running them through a term-frequency, inverse document frequency (tf-idf) algorithm to figure out which words in a story are important, and thus a likely indicator of what the story is about.
For the non-nerds among us, tf-idf basically counts how many times a word appears in an article and compares that number to the number of times it appears in other stories in a given set. The fewer articles a word appears in, the more importance it is assigned in the current article. In addition to the standard tf-idf algorithm, Clavis also adds extra weight proper nouns and more weight still if those nouns they appear earlier in a story."
According to Knight Lab: "In April, Washington Post announced that it had set a new single-month traffic record, with more than 52 million unique visitors. The figure represented not only a new record, but also a 65 percent year-over-year gain that led other big-name publishers, according to the Post. Publisher Frederick J. Ryan praised the addition of new editorial staffers and awards, and then called special attention to engagement: While unique visitors were up 65 percent, pageviews were up 109 percent year-over-year." This was achieved through the help of a recommendation system, Clavis.