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Connected articles is a system that is designed to find articles similar or linked to articles a user has already clicked on. This can be incredibly useful as users often search in an imprecise way and if they only look at a page or two of results they may miss some important articles. Connected articles looks for connections between document and helps unearth hidden/missed gems!

Connected uses three sources of information to find the connections:

  • Clickstream data – when a user searches and clicks on more than one document, we infer that the documents clicked are connected by the users intention.
  • Citation data – for every document clicked we explore articles that the article cited (as references in the document) and we also looked for articles that have cited document as well. This is also known as forward and backward citation searching. This data is restricted to articles that have a DOI (which is over 95%)
  • Related articles – we use the data from PubMed’s “similar articles” feature and grab the top 20 articles deemed to be most semantically similar to the article(s) clicked.

We take these three types of connections and combine them, using a special algorithm, to create a list of results. Those deemed most closely connected – using all three sources – appear at the top:

NOTE: Free users of Trip only see the first three results. Pro subscribers get no such restriction.