Case Study

Recommendation Engine that Enhances User Experience
and Increases Time Spent on a Website

Business Objective

Our client is a large media company in Hong Kong that runs a daily news website, along with other content businesses.

Their objective was to develop a recommendation engine to enhance the user experience by creating personalized article recommendations for each known user. By presenting a user with articles that are relevant to them, the user will read more articles, thus spending more time on the site.


  • Text of the articles was in Chinese – articles for the last 5 years available
  • More than 1 million users access the website everyday – session log data available
  • Articles can be editorial (once a day) or from a real-time feed.

Solution Methodology

We used a content-based clustering approach to build the recommendation engine.

  • Determined a language model to represent documents in a structured manner
  • Evaluated multiple similarity metrics, clustering algorithms, n-gram tokens
  • Clustered similar news articles in each category, for a given period. Also, determined the optimal number of clusters.
  • Evaluated various scoring approaches to classify new articles into the aforementioned clusters
  • Analyzed a user’s history to find the clusters they have shown interest in the past
  • Used the above to recommended appropriate articles to specific users
  • Delivered the entire solution on an AWS 5-node cluster running Cloudera Hadoop

Business Impact

  • The recommendation engine resulted in relevant article/video recommendations which enhanced the user experience and increased time on the website. Furthermore, it reduced editorial workloads.
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