RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Abstract

Recommendation (recommender) systems have played an increasingly important role in both research and industry in recent years. In the area of publication data, for example, there is a strong need to help people find the right research information through recommendations and scientific reports. The difference between search engines and recommendation systems is that search engines help us find something we already know, while recommendation systems are more likely to help us find new items. An essential function of recommendation systems is to support users in their decision making. Recommendation systems are information systems that can be categorized into decision support systems, as long as they are used for decision making and are intended to support people instead of replacing them. This paper deals with recommendation systems for research information, especially publication data. We discuss and analyze the challenges and peculiarities of implementing recommender systems for the scientific exchange of research information. For this purpose, data mining techniques are examined and a concept for a recommendation system for research information is developed. Our aim is to investigate to what extent a recommendation system based on a collaborative filtering approach with cookies is possible. The data source is publication data extracted from cookies in the Web of Science database. The results of our investigation show that a collaborative filtering process is suitable for publication data and that recommendations can be generated with user information. In addition, we have seen that collaborative filtering is an important element that can solve a practical problem by sifting through large amounts of dynamically generated information to provide users with personalized content and services.