Our paper titled “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport” is accepted for publication at the 15th International Symposium on Information Science (ISI) in Berlin.

Abstract:

Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel search, however, these recommendation approaches have proven their effectiveness. We were interested to find out how stereotype and most-popular recommendations would perform in the scenario of a digital library. Therefore, we implemented the two approaches in the recommender system of GESIS’ digital library Sowiport, in cooperation with the recommendations-as-a-service provider Mr. DLib. We measured the effectiveness of most-popular and stereotype recommendations with click-through rate (CTR) based on 28 million delivered recommendations. Most-popular recommendations achieved a CTR of 0.11%, and stereotype recommendations achieved a CTR of 0.124%. Compared to a “random recommendations” baseline (CTR 0.12%), and a content-based filtering baseline (CTR 0.145%), the results are discouraging. However, for reasons explained in the paper, we concluded that more research is necessary about the effectiveness of stereotype and most-popular recommendations in digital libraries.

Read the pre-print version: http://beel.org/publications/2017%20ISI%20–%20Stereotype%20and%20Most-Popular%20Recommendations%20in%20Sowiport%20–%20preprint.pdf

Download the dataset: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HFIV1A