PyTerrier-based Research Data Recommendations for Scientific Articles in the Social Sciences

Research data is of high importance in scientific research, especially when making progress in experimental investigations. However, finding appropriate research data is difficult. One possible way to alleviate the situation is to recommend research data to scholarly search system users based on the research articles they are searching. With LiLAS, the lab organizers provide the opportunity i) to present such recommendations to users of the live system GESIS Search and ii) to evaluate the experimental recommender system in this live system with its actual users. As part of our participation in LiLAS, we computed a simple method for recommending research data and evaluated our approach in two rounds each lasting approximately one month. For our approach, we applied the classical TF-IDF method to rank the research data by their relevance to existing publications. We measure our method’s usefulness using user feedback, i.e., simple clicks on the recommendations. In both rounds, our experimental system obtained almost the same outcomes as the baseline.