Increasingly sophisticated content management systems (CMS) allow librarians to publish content via the web and within the private domain of institutional learning management systems. “Libraries as publishers” may bring to mind roles in scholarly communication and open scholarship, but the authors argue that libraries’ self-publishing dates to the first “pathfinder” handout and continues today via commonly used, feature-rich applications such as WordPress, Drupal, LibGuides, and Canvas. Although this technology can reduce costly development overhead, it also poses significant challenges. These tools can inadvertently be used to create more noise than signal, potentially alienating the very audiences we hope to reach. No CMS can, by itself, address the fact that authoring, editing, and publishing quality content is both a situated expertise and a significant, ongoing demand on staff time. This article will review library use of CMS applications, outline challenges inherent in their use, and discuss the advantages of embracing content strategy.

Libraries are increasingly embracing user experience (UX) and user-centered design principles to improve the satisfaction and success of library users. Electronic resources management can utilize such principles to better support users as they interact with the library"s website and its employ to improve the user experience. These strategies include utilizing basic UX principles when designing sites and interfaces; analyzing quantitative data to inform the library on how such sites are being used; recruiting strategies for library user studies; and, finally, a call to move to a more unified user experience and to work more closely with vendors on improvements to help users succeed.

Purpose This paper introduces a machine learning based "My Account" recommender for implementation in open discovery environments such as VuFind, among others.Design/methodology/approach The approach to implementing machine learning based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems.Findings The authors discuss the need for large data sets from which to build an algorithm; and introduce a prototype recommender service, describing the prototype's data flow pipeline and machine learning processes. Practical implicationsThe browse paradigm of discovery has neglected to leverage discovery system data to inform the development of personalized recommendations, with this paper, the authors show novel approaches to providing enhanced browse functionality by way of a user account. Originality/valueIn the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development of personalized recommendations.

Web content strategy is a relatively new area of practice in industry, in higher education, and, correspondingly, within academic and research libraries. The authors conducted a web-based survey of academic and research library professionals in order to identify present trends in this area of professional practice by academic librarians and to establish an understanding of the degree of institutional engagement in web content strategy within academic and research libraries. This article presents the findings of that survey. Based on analysis of the results, we propose a web content strategy maturity model specific to academic libraries.