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A guide on how to use Keenious, an AI based tool that generates article suggestions based on an analysis of a text.

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How does Keenious rank articles?

To help users identify how Keenious determines the relevance of a particular paper, the Results Insight feature provides some additional information that explains the ranking of a particular paper. Focusing on factors such as text relevance, publication date and citation count, Results Insight provides a degree of transparency about search results that is lacking in some traditional search engines.

Accessing Results Insight


search result on Keenious indicating a small graph icon in a red box that provides access to Results Insight


To access the Result Insights feature, simply follow these steps:

  1. Conduct a search using Keenious to find articles relevant to your query.
  2. In the search results, each article will be displayed on a result card.
  3. On the result card, look for the Result Insights button. It's located at the top right, next to the cite and bookmark buttons (see image above).
  4. Click the Result Insights button to view insights on why the article was recommended (see below).


Example of a Results Insights screen showing Text Relevance, Recency Boost and Citation Boost


How it works


Text Relevance

This indicates the balance between two primary analyses conducted by Keenious to assess the relevance of an article based on your input text. It shows the balance, not an absolute relevance score, because relevance scores can significantly vary across different searches. It's also important to note that Keenious evaluates only the title and the abstract of articles to judge their relevance.

  • Shared Terms: This highlights terms that appear in both your input text and the article's title or abstract. The importance of a term is influenced by its rarity and frequency. If shared terms dominate the balance, it shows that Keenious finds specific words and terms in your search more important within the article's title or abstract.
  • AI Predicted Meaning: This relevance is determined through AI analysis with a Large Language Model, comparing text embeddings from your search text and the article's text. If AI Predicted Meaning accounts for most of the relevance, it suggests the article's theme and topic are considered relevant by Keenious, even if the exact words are different.


Image shows Text Relevance information, showing percentage for shared terms and percentage AI predicted meaning.



Recency Boost

Keenious increases the scores of newer articles to highlight the latest research. This increase is a percentage added to the article's final score. The more recent the article, the bigger the boost it gets (up to a maximum of 5%). For example, an article was published within the last year gets a higher boost than articles published more than ten years ago. As a result, older articles are less likely to appear near the top of your results.


Image showing Recency Boost, in this a case a 2.5% boost on a 2020 article.



Citation Count Boost

As there are many articles within the dataset that have zero citations, Keenious provides a boost articles that are cited. This boost is similar to the recency boost but focuses on the number of times an article has been cited according to the OpenAlex dataset. A small number of citations can significantly increase an article's score, but this boost levels off at 100 citations with a maximum boost of 6%. Beyond this point, additional citations don't increase the boost.


Image showing Citation Count Boost. In this case, a 3.7% boost for 12 citations.