Multi-dimensional Ratings for Research Paper Recommender Systems: A Qualitative Study

Shaikhah Alotaibi (1) , Julita Vassileva (1)

1 U of S - University of Saskatchewan [Saskatoon, Canada]

Research paper recommender systems (RSs) aim to alleviate information overload for researchers. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a focus group qualitative study to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigate also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria.

Mots clés: Research paper recommender system; multi-dimensional rating; collaborative filtering; user-centric design

Domaines: Informatique [cs]

Shaikhah Alotaibi, Julita Vassileva. Multi-dimensional Ratings for Research Paper Recommender Systems: A Qualitative Study. International Symposium on Web AlGorithms, Jun 2015, Deauville, France. ⟨hal-01171132⟩