@inproceedings{ubo:46601,
  author = {Loepp, Benedikt and Hussein, Tim and Ziegler, Jürgen},
  chapter = {},
  title = {Choice-based Preference Elicitation for Collaborative Filtering Recommender Systems},
  year = {2014},
  pages = {3085–3094},
  publisher = {ACM},
  address = {New York, NY, USA},
  isbn = {978-1-4503-2473-1},
  doi = {10.1145/2556288.2557069},
  abstract = {We  present  an  approach  to  interactive  recommending  that combines the advantages of algorithmic techniques with the benefits  of  user-controlled,  interactive  exploration  in  a novel  manner.  The  method  extracts  latent  factors  from  a matrix of user rating data as commonly used in Collaborative Filtering, and generates dialogs in which the user iteratively chooses between two sets of sample items. Samples are chosen by the system for  low and high values of each latent  factor  considered. The method  positions  the  user  in the latent factor space with few interaction steps, and finally selects items near the user position as recommendations. 
 In a user study, we compare the system with three alternative  approaches  including  manual  search  and  automatic recommending. The results show significant advantages of our  approach  over  the  three  competing  alternatives  in  15 out of 24 possible parameter comparisons, in particular with respect to item fit, interaction effort and user control. The findings  corroborate  our  assumption  that  the  proposed method  achieves  a  good  trade-off  between  automated  and interactive functions in recommender systems.},
  url = {https://dl.acm.org/doi/10.1145/2556288.2557069?cid=87958660357},
  booktitle = {Proceedings of the 32nd International Conference on Human Factors in Computing Systems (CHI ’14)}
}