Paper Title:

Intelligent Recommendation Engine


Shailendra Singh Kathait, Shubhrita Tiwari, Piyush Kumar Singh


Highly engaged visitor is central to the success of any Digital publisher. Having right content and serving it to the right audience at right time is the key to achieving high engagement rates. Publishers have used machine learning based recommendation engines to summarise articles, extract article metadata (keywords, phrases), build user preferences and recommend right content to the visitor in real time on a website, as email digest or app notifications. This has been achieved through collaborative filtering, content-based filtering. Recommendation algorithms based only alone on collaborative filtering or content based filtering overlook important factors that drive suitable recommendations for a user. This paper describes the application of hybrid approach i.e. Collaborative Filtering together with Content-based Filtering for making the most appropriate and relevant recommendations.

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