How Keenious recommends research articles
Keenious is a tool that recommends articles and topics based on your document. This guide explains how Keenious works and how to use it effectively, regardless of your technical background. We update the guide to reflect any changes to the Keenious algorithm.
At Keenious, we believe in transparency and want to help our users understand how our system works. This will increase their confidence in using our tool and enable them to get the best results. Understanding our system can also help users to identify why they are not getting the results they expected.
By being open and honest about our processes, we hope to set an example for the rest of the Artificial Intelligence industry. We believe that transparency benefits both the companies creating the tools and the end users who rely on them.
The 4 key factors that influences the ranking of an article
1. Shared terms
Keenious analyzes your document to identify the number of shared rare terms between your text and the articles' title and abstract. Rare terms are significant because they represent specialized vocabulary and concepts that could be essential to your research. By identifying shared rare terms, Keenious can predict the relevance of an article to your specific needs.
Shared terms are a well-established and widely accepted method for information retrieval systems to identify relevant articles. In fact, academic databases such as JSTOR and Pubmed rely on shared terms to retrieve pertinent articles. By utilizing shared terms, Keenious is able to leverage a proven approach that is effective in practice.
2. The predicted meaning of your text
When recommending articles and topics, Keenious not only considers shared terms between your document and the articles' title and abstract, but also predicts the meaning and topic of your text using a language model. This allows us to identify articles that are most relevant to your specific needs based on the language and context of your document.
Predicting the meaning/topic of your text is useful because it allows us to identify articles that may not share many rare terms with your document but are still highly relevant. For example, an article that is highly relevant to your topic may use different terminology or vocabulary than your document. By predicting the meaning and topic of your text, we can identify articles that address the same topic even if they use different terminology.
In addition, predicting the meaning of your text helps to avoid false positives, which can occur when articles have a high number of shared rare terms with your document but are not actually relevant to your needs. By considering the meaning and context of your document, Keenious is able to provide highly relevant articles and topics that are tailored to your specific needs.
3. Citation count
Keenious takes into account the number of times that an article has been cited by other articles. While citation count can be a useful measure of an article's authority and importance within its field of study, it's important to note that it can also have some limitations. For example, citation count may not be an accurate reflection of an article's quality, as some articles may be cited frequently but not necessarily for their academic merit. Additionally, citation count can be influenced by factors such as the age of the article, the popularity of the journal, and the citation practices within a particular field.
4. Recency of Publication
Keenious also factors in the publication date of articles, giving precedence to more recent papers. This ensures that users are presented with up-to-date research findings and developments in their area of interest.
Keenious offers a "highlighted search" feature that enables users to highlight specific pieces of text within their document, and obtain a separate analysis of these highlighted portions. This feature is particularly useful when users want to emphasize certain sections of their text or focus their search on a particular topic.
When users highlight a piece of text, Keenious performs a separate analysis of that text, taking into account the unique context and content of the highlighted portion. The scoring of this analysis is then represented in the ranking information modal, which allows users to see if a particular result is coming up because it is mostly relevant to the highlighted text, the overall document, or a combination of both.
In conclusion, we offer a tool that recommends articles and topics based on the content of a user's document. Our tool utilizes a range of factors to predict the relevance of articles to the user's needs, including shared rare terms, predicted meaning and citation count.
We believe in transparency and open communication with our users. By providing users with a clear understanding of how our tool works, we help to increase user confidence and enable users to get the best results. This approach also helps users to identify areas for improvement in their own document writing and research practices.
Overall, we aim to set an example for the artificial intelligence industry by demonstrating the benefits of transparency and user-centric design. Through our commitment to user satisfaction, we strive to transform the way users approach information retrieval and research.