Personalization in research is quickly gaining favor among academics who are struggling with literature search and reading. If it is possible to type in some keywords and get relevant AI-driven recommendations on a shopping site, why not use such AI in research? Personalized recommendations would be invaluable to the millions of researchers who spend hours every week online navigating millions of links across a number of databases to find new, relevant research articles or publications. In fact, an Elsevier report showed that apart from robbing researchers of valuable time, this effort often counts for nothing as almost half of the shortlisted articles are not actually useful!1 This is where personalization in research recommendations is being seen as the smart solution, allowing researchers to focus on reading articles rather than spending vast amounts of time searching for the right content.
The need for personalization in research
Given the sheer number of research papers published every year, it can be difficult to know where and how to start research reading. A researcher who is already hard pressed for time can ill-afford to start from scratch every time they want to read relevant research papers. Even when researchers are able to identify pertinent scholarly literature, they often struggle to determine its quality or relevance, often having to spend a significant amount of time reading through them. The manual nature of this process presents a high possibility of researchers either missing important information or overlooking critical details, both of which could lead to incomplete or incorrect conclusions.
Another challenge researchers face is the lack of standardization across research resources. Different publishers and databases use different indexing systems and citation styles, which can make it difficult to search for resources effectively. This can lead to frustration and wasted time, as researchers may need to use multiple databases and search engines to find the information they need. Furthermore, many research databases and search engines rely on keyword-based searches, which can be imprecise and may not capture the nuances of a particular research topic. This can result in irrelevant or low-quality results, which can hinder the research process.
Personalized recommendations can go a long way in solving the problem of recurring search for researchers by giving them latest, relevant, and tailored content that is specifically based on their topic of interest and older search history. It is like getting recommendations once you finish buying a new book or even after watching a movie on any OTT platform.
Using personalization in research to boost productivity
Today, the rapid development of AI in research has revolutionized the way we search for and consume scholarly literature. However, there is much room for improvement in the solutions available to researchers today. Tools based on simple search algorithms result in users having to go through the process of searching for research each and every time; even then, they’d keep getting the same kind of results unless they try various search keywords and phrases. In a perfect world, each session would be different for users and throw up refined results that are useful and relevant to where the researcher is in his/her reading journey. With personalization in research, this perfection can be achieved.
Apart from the earlier search experience, new smart technologies based on AI in research has led to the rapid rise of recommendation tools. It is the addition of personalization in research that makes these tools different and better. The algorithm does the work for you, eliminating the need for individuals to spend hours searching for research. Not only can personalization in research suggest relevant research papers or articles based on your chosen topics, but it will also recommend additional topics or areas that you may not be consciously looking for. By learning from the user’s reading history and actions (what they’re reading, looking for, and interested in), these tools can intuitively recommend more relevant, targeted research papers that help academics improve productivity and progress faster on their own work.
How R Discovery takes personalization in research to the next level
A literature search and research reading app that aims to make research accessible to academics worldwide, R Discovery is a great example of personalization in research. The AI-powered tool has enabled excellent personalization in research by developing a robust AI engine that can quickly scan and find research papers best matched to user interests. This smart tool stands out for its ability to understand user data and feedback to generate better more personalized recommendations, even tapping related topics and journals that may be relevant but are not being followed by the researcher.
Users only need to share their chosen topics, research interests, or preferred journals once and R Discovery’s advanced AI algorithm jumps into action. It instantly creates a reading feed, with personalized recommendations on the latest, most relevant picks from a continually growing content bank of over 110 million research articles, open access papers, preprints, and more across all major research disciplines. The app’s interface is user-friendly and intuitive, users can easily browse through personalized recommendations, specially curated feeds, shared reading lists, search and refine results using smart filters, and even save articles to read later. The tool also presents users with audio streaming, research paper translation, collaboration options, and reference manager auto syncs that make life even easier for researchers. R Discovery’s efforts to maximize personalization in research has paid off, with over 2.5 million researchers across the world relying on the platform to save time and effort. By solving the problem of searching for research, R Discovery has truly revolutionized the literature search and research reading experience for academics everywhere.
References:
- Trust in Research. Research Survey by Elsevier and Sense About Science, June 2019. Available online at https://www.elsevier.com/__data/assets/pdf_file/0011/908435/Trust_evidence_report_summary_Final.pdf
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