@inproceedings{LiuGuoDakotaetal.2017,
  author    = {Can Liu and Chun Guo and Daniel Dakota and Sridhar Rajagopalan and Wen Li and Sandra K{\"u}bler and Ning Yu},
  title     = {“My Curiosity was Satisfied, but not in a Good Way”: Predicting User Ratings for Online Recipes},
  series = {Proceedings of the Second Workshop on Natural Language Processing for Social Media in conjunction with COLING-2014 (SocialNLP 2014). Dublin, Ireland. August 24, 2014},
  editor    = {Shou-de Lin and Lun-Wei Ku and Erik Cambria and Tsung-Ting Kuo},
  isbn      = {978-1-873769-45-4},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-61863},
  pages     = {12 -- 21},
  year      = {2017},
  abstract  = {In this paper, we develop an approach to automatically predict user ratings for recipes at Epicurious.com, based on the recipes’ reviews. We investigate two distributional methods for feature selection, Information Gain and Bi-Normal Separation; we also compare distributionally selected features to linguistically motivated features and two types of frameworks: a one-layer system where we aggregate all reviews and predict the rating vs. a two-layer system where ratings of individual reviews are predicted and then aggregated. We obtain our best results by using the two-layer architecture, in combination with 5 000 features selected by Information Gain. This setup reaches an overall accuracy of 65.60\%, given an upper bound of 82.57\%.},
  language  = {en}
}