3 citations · 5 across the 6 of their papers we have counts for
6 papers
Food Pairing Unveiled: Exploring Recipe Creation Dynamics through Recommender Systems
Giovanni Palermo, Claudio Caprioli, Giambattista Albora
In the early 2000s, renowned chef Heston Blumenthal formulated his "food pairing" hypothesis, positing that if foods share many flavor compounds, then they tend to taste good when…
Machine learning-based similarity measure to forecast M&A from patent data
Giambattista Albora, Matteo Straccamore, Andrea Zaccaria
Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will ma…
Which products activate a product? An explainable machine learning approach
Massimiliano Fessina, Giambattista Albora, Andrea Tacchella +1
Tree-based machine learning algorithms provide the most precise assessment of the feasibility for a country to export a target product given its export basket. However, the high nu…
Sapling Similarity: a performing and interpretable memory-based tool for recommendation
Giambattista Albora, Lavinia Rossi-Mori, Andrea Zaccaria
Many bipartite networks describe systems where an edge represents a relation between a user and an item. Measuring the similarity between either users or items is the basis of memo…
Machine learning to assess relatedness: the advantage of using firm-level data
Giambattista Albora, Andrea Zaccaria
The relatedness between a country or a firm and a product is a measure of the feasibility of that economic activity. As such, it is a driver for investments at a private and instit…
Product Progression: a machine learning approach to forecasting industrial upgrading
Giambattista Albora, Luciano Pietronero, Andrea Tacchella +1
Economic complexity methods, and in particular relatedness measures, lack a systematic evaluation and comparison framework. We argue that out-of-sample forecast exercises should pl…