Title
Improving Recommender Systems Beyond the Algorithm.
Abstract
Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that this algorithmic focus is myopic. In particular, since learning algorithms generally improve with more and better data, we propose shaping the feedback generation process as an alternate and complementary route to improving accuracy. To this effect, we explore how changes to the user interface can impact the quality and quantity of feedback data -- and therefore the learning accuracy. Motivated by information foraging theory, we study how feedback quality and quantity are influenced by interface design choices along two axes: information scent and information access cost. We present a user study of these interface factors for the common task of picking a movie to watch, showing that these factors can effectively shape and improve the implicit feedback data that is generated while maintaining the user experience.
Year
Venue
Field
2018
arXiv: Human-Computer Interaction
Information foraging theory,Recommender system,Information scent,User experience design,Computer science,Information access,Algorithm,Human–computer interaction,User interface,Multimedia,Interface design
DocType
Volume
Citations 
Journal
abs/1802.07578
0
PageRank 
References 
Authors
0.34
26
3
Name
Order
Citations
PageRank
Tobias Schnabel12019.81
Paul N. Bennett2150087.93
Thorsten Joachims3173871254.06