graphic of people working with technology

Als Verbraucher:innen erleben wir Personalisierung täglich durch gezielte Online-Werbung beim Surfen oder in unseren Social-Media-Konten. Wir sehen es in den Shows und Filmen, die Netflix und Amazon uns vorschlagen, und in der Musik, die Spotify oder Pandora empfehlen, während wir durch unsere Playlists scrollen. Diese Technologie basiert auf unserem bisherigen Kauf- und Nutzungsverhalten. Was uns gefallen hat, nicht gefallen hat, wem wir folgen und wonach wir zuvor gesucht und was wir schließlich gekauft haben, wird vom maschinellen Lernen verwendet, um vorherzusagen – und vorzuschlagen – was für uns in Zukunft von Interesse sein könnte. Aber wie wirken sich Personalisierungstechniken auf die Entdeckung wissenschaftlicher Inhalte und das Informationsmanagement aus? 

Lesen Sie mehr in dem folgenden Beitrag, der zunächst im Blog des CCC erschien. 

The following is an excerpt from Accessing and Analyzing Relevant Content in Today’s Information Chaos.   

According to the latest data, approximately 8.5 billion searches are conducted each day on Google. Google — the tool, the term, and the technology — is omnipresent in our collective global culture. This has caused a fundamental change in what individuals expect from search results. Our private life search habits and expectations have unsurprisingly spilled over into our business life.  

Where personalization comes into play 

With this shift in expectations, it’s important to understand the different types of personalization used by search engines so we can recognize the benefits of applying these tools to data searches in the business environment as well.   

When users are searching through and finding content, personalization allows them to find relevant content faster by moving artificial intelligence (AI)-informed recommendations to the top. With explicit personalization, search results are driven by a user’s chosen preferences, such as setting specific data source selection and/or setting source “favorites.” Based on these choices, the user expects more relevant search results to appear. Implicit personalization delivers personalized content recommendations based upon a user’s past actions and behaviors.  

Research indicates 75% of people will never scroll past the first page on a Google search, drastically limiting the range of potential information. In light of this data, it is more important than ever that the information most highly relevant to the individual researcher appears at the top of search results.  

Challenges and opportunities 

At R&D intensive companies, the questions that researchers and other employees attempt to answer are far more complex than a simple Google query can answer. For example, what is most relevant to a researcher working on a promising early-stage drug candidate for Fibrodysplasia ossificans progressive — otherwise known as FOP or Stoneman’s Disease, which is expected to affect only 4,000 individuals worldwide — is quite different from what that the same researcher would find valuable in the mature diabetes market.   

Why? The Rare Disease field is known for its small patient populations, premature disease understanding, and overwhelming lack of education. Comprehensive and relevant information may be very difficult to find and would require content discovery solutions that scour scientific literature, patent information, real-world evidence, and patients’ lived experiences from as many sources as possible, including scientific societies and congresses, scholarly publications, clinical trials, social media platforms, etc. Casting as broad a net as possible would help generate novel insights and new discoveries and drive results in this market.   

In contrast, let’s look at the diabetes market. Diabetes was accurately described for the first time in the 2nd century A.D.; by January 1922, the first insulin injection was given to a 14-year-old boy dying of the disease. For the last 100 years, diabetes has been studied by countless principal investigators, labs, and drug development companies. The sheer volume of clinical and observational data and content is vast, and as a result, this researcher’s challenge becomes one of technical relevancy and prioritization. R&D users in the diabetes field need solutions and methods to narrow down and contextualize information, to manage the deluge of data, and to help them recognize newly established patterns and trends.   

Relevancy in scientific, medical, and technology search  

An article’s median half-life (more than one-half its total downloads) across all publishers was between 2 and 4 years. This can bias traditional search engines to favor older publications because citations, impact factor, etc. can take years to develop — missing the mark in identifying potentially novel discoveries and innovation.   

This leads us to recognize why R&D professionals need software solutions that are based on the right kind of machine learning — in particular, implicit and explicit personalization tools that better “understand” a user’s goals and result in more serviceable content discovery, regardless of the lifespan of a significant scientific paper. Using the right machine learning tool will combine implicit and explicit personalization with the right content to find relevant results.   

One user of CCC’s RightFind Navigate at a global pharmaceutical company recognizes the value of creating a unified search experience from disparate, siloed content from trusted internal and external sources, saying, “RightFind offers a tremendous benefit as a place to go when you don’t know where to start.”   

Breakthroughs in cancer treatments, rare diseases, and rocket science are possible, not because individual experts know everything on the subject but because people can draw knowledge that does not reside in their own heads— which makes finding the most relevant information at the right time so critical to drive innovation.  

Keep reading Accessing and Analyzing Relevant Content in Today’s Information Chaos.   

Learn more about personalized search across multiple sources of data and information for highly relevant discovery with RightFind Navigate

Author: RD

RightsDirect, eine Tochtergesellschaft von Copyright Clearance Center, bietet fortschrittliche Informations- und Datenintegrationslösungen für Organisationen in ganz Europa und Asien. Als Pionier der freiwilligen kollektiven Lizenzierung ist CCC ein führender Anbieter von Informationslösungen für Organisationen auf der ganzen Welt. Mit umfassender Fachkompetenz in den Bereichen Urheberrecht, Technologie, Fachinhalte, PIDs, FAIR-Datenprinzipien, Metadaten und mehr arbeitet CCC daran Urheberrechte zu stärken, den Austausch von Wissen zu beschleunigen und Innovationen voranzutreiben. CCC und seine Tochtergesellschaft RightsDirect unterstützen Unternehmen dabei, die Leistungsfähigkeit von Daten, KI und maschinellem Lernen zu nutzen, um strategische Entscheidungen zu treffen, ihr Geschäft auszubauen und Wettbewerbsvorteile zu erlangen.