Overcoming the Long Tail Problem: A Case Study on CO2-Footprint Estimation of Recipes using Information Retrieval
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Abstract
We propose approaches that use information retrieval methods for the automatic calculation of CO2-footprints of cooking recipes. A particular challenge is the "long tail problem" that arises with the large diversity of possible ingredients. The proposed approaches are generalizable to other use cases in which a numerical value for semi-structured items has to be calculated, for example, the calculation of the insurance value of a property based on a real estate listing. Our first approach, ingredient matching, calculates the CO2-footprint based on the ingredient descriptions that are matched to food products in a language resource and therefore suffers from the long tail problem. On the other hand, our second approach directly uses the recipe to estimate the CO2-value based on its closest neighbor using an adapted version of the BM25 weighting scheme. Furthermore, we combine these two approaches in order to achieve a more reliable estimate. Our experiments show that the automatically calculated CO2-value estimates lie within an acceptable range compared to the manually calculated values. Therefore, the costs of the calculation of the CO2-footprints can be reduced dramatically by using the automatic approaches. This helps to make the information available to a large audience in order to increase the awareness and transparency of the environmental impact of food consumption.