Following the logic of Zavalina and Vassilieva in Understanding the Information Needs of Large-Scale Digital Library Users (2014), I think information retrieval (IR) systems should be informed by the information-seeking behaviors of the user community. This ensures that the IR system is designed with the users in mind and that the main purpose of the system is to help users acquire their informational needs. As a principle of design, this is also necessary if the system is to have a democratizing effect. You want to have an IR system that empowers the user, allowing them to easily navigate the interface and satisfy their needs through an intuitive and smart system. This seems pretty much like the ideal.
But saying an IR system should be “informed” by user behavior is different from saying that an IR system should “adapt” to user behavior. The former presupposes that the IR system designers understand and can predict the searching habits of individuals. They would then try to accommodate a wide range of user search styles through the implementation of useful tools, like relevance rankings or context help. Adapting a system around users, however, means that the IR system you would get would look like something akin to Google, where popularity and site traffic dictate what will be optimized.
Of course, it is no secret among LIS professionals that search skills among the general population suffer from a lack of information literacy and specific knowledge of IR systems and how the system retrieves user inputted keywords. Khapre and Basha in A Theoretical Paradigm of Information Retrieval in Information Science and Computer Science (2012) mentioned the principle of least effort. While the idea inherent in the principle of least effort is from the design perspective meant to optimize retrieval based on limited user knowledge, the phenomenon of least effort in information-seeking behavior is still problematic. In a matching program, where a user comes up with a query which is analyzed and matched to a document by organized keywords, broad and unfocused keywords will yield fuzzy search results.
Therefore an IR system cannot adapt to users without sacrificing its functionality for precision. An IR system must be able to handle very specific intellectual queries at a very granular level. I think this question poses a central dilemma in the field of information retrieval and access. Indeed, there is a lot of cognitive dissonance between “man and machine,” as it were. User expectations are way too high. People have become spoiled with the ease of performing Google searches and obtaining instant results to whatever research requirements they have. But I think it is important to realize that IR systems are sophisticated tools that require a sophisticated understanding of how to use them. In Khapre and Basha’s article, they pointed out that technology can change our thoughts and, importantly, that “technology is making it difficult for users to recognize that it is external, known only to the simple “interface value””. This concept of interface value is an important one in human-computer interaction, because users have expectations of the IR system which they take at “interface value.” But they are completely ignorant of the internal coding of the IR system, which is considerably complex and based on algorithmic science that usually escapes the end user’s interest or opportunity for study.