Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

UTILIZING THE POTENTIALS OF BIG DATA IN LIBRARY ENVIRONMENTS IN NIGERIAN FOR RECOMMENDER SERVICES

The big data revolution has gained global attention and initiated creative innovations in every field and libraries as engines of access to information have also been affected by this new trend. Libraries in this part of the world have not utilized the amazing potential of big data in library services. In this time, when various terms such as algorithms age, petabytes age, data age, etc. are been used to describe the activities initiated by machine learning, industries and organizations can achieve much by incorporating inspiring and innovative tools to improve services and performance. In this vein libraries in Nigeria are expected against all odds to make their services more interactive, attractive, innovative, and exciting by utilizing cloud technologies and machine learning techniques to create recommender services. This paper titled “Utilizing the Potentials of Big Data in Nigeria Library Environments by Recommender Services”, focuses on the concept and characteristics of big data and its importance in complementing traditional library services, areas for applying big data systems in libraries, the concept of recommender systems and how it works, adopting recommender systems in libraries for maximum benefits, tools, and techniques for setting up big data recommender systems in libraries, challenges of big data recommender systems in libraries in Nigeria and strategies for overcoming big data challenges in library systems. The paper is based on a contextual analysis of literature from various scholarly works. The paper will also proffer recommendations based on the study

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio