Expert advice on how ingredients can be replaced in recipes is widely available on-line. However, these are general substitution rules, which do not take into account contextual factors such as culture, sensory perception, season, etc. We aim at tuning general rules to particular recipes. From an on-line food encyclopedia we extract explicit substitution rules. We also consider implicit substitution rules, derived by the categorisations in the same source. By applying Latent Dirichlet Allocation (LDA) onto a crawled dataset, we rank ingredients based on their likelihood of being interchangeable, given a recipe. The results show that our statistical approach can approximate manual judgments.
|Title of host publication||Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp '14 Adjunct|
|Publication status||Published - 2014|
|Event||UbiComp '14 - |
Duration: 1 Jan 2014 → 1 Jan 2014
|Period||1/01/14 → 1/01/14|