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Overcoming the Long Tail Problem: A Case Study on CO2-Footprint Estimation of Recipes using Information Retrieval

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.