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In this paper, the author has tried to outline the main ideas in connection with what the author conceives to be the university of the future, a university that should not only educate people within the university system but also prepare them to fill specific job positions at both local and global levels, apart from necessarily providing them with the critical thinking and competences in autonomous learning that will make them flexible and capable of adapting to the job market and to a fast-changing world in general.

The author has revised some of the major issues that are going to determine the direction of the university of the future, i.e. the employment opportunities of tomorrow; the role of new technologies, especially the impact of artificial intelligence (AI); quality in higher education; and internationalization.

The author has also pointed out the importance of the technologies and the great role they indisputably play in present and future education at all levels, a fact that has been particularly and hugely enhanced and promoted by the COVID-19 pandemic situation, thereby facilitating and fostering distance learning. This is very much connected to the application of AI to higher education, another unavoidable issue of utmost importance for the university of the future. While these technological advances present a challenge to universities, which must determine which are necessary and desirable and how to implement them, it is, ultimately, our responsibility to use them, in an ethical way, to the benefit of our students. The university of the future also has to be of high quality, and this involves carrying out important and decisive action having to do with matters of inclusion, hiring policies and the expansion of international opportunities for all parties involved.

This paper outlines the main ideas in connection with what the author conceives to be the university of the future, a university that should not only educate people within the university system but also prepare them to fill specific job positions at both local and global levels, apart from necessarily providing them with the critical thinking and competences in autonomous learning that will make them flexible and capable of adapting to the job market and to a fast-changing world in general. Moreover, the role of new technologies (especially the impact of AI), quality and internationalization are also discussed as relevant factors in this view of the university of the future.

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.