In this paper we describe the design and implementation of non-personalized recommendations in the PATHS system. This system allows users to explore items from Europeana in new ways. Recommendations of the type “people who viewed this item also viewed this item” are powered by pairs of viewed items mined from Europeana. However, due to limited usage data only 10.3 % of items in the PATHS dataset have recommendations (4.3 % of item pairs visited more than once). Therefore, “related items”, a form of content-based recommendation, are offered to users based on identifying similar items. We discuss some of the problems with implementing recommendations and highlight areas for future work in the PATHS project.
|Name||Communications in Computer and Information Science|
- Digital libraries