Appetitoso: A Search Engine for Restaurant Retrieval based on Dishes
Authors: Gianni Barlacchi, Azad Abad, Emanuele Rossinelli, Alessandro Moschitti (Università di Trento, TIM, Kloevolution srl, Qatar Computing Research Insititute, 2016)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Recent years have seen an impressive development and diffusion of web applications to food domains, e.g., Yelp, TripAdvisors. These mainly exploit text for searching and retrieving food facilities, e.g., \emph{restaurants}, \emph{caff\'e}, \emph{pizzerias}. The main features of such applications are: the location and quality of the facilities, where quality is extrapolated by the users' reviews. More recent options also enable search based on restaurant categorization, e.g., \emph{Japanese}, \emph{Italian}, \emph{Mexican}. In this work, we introduce Appetitoso, an innovative approach for finding restaurants based on the dishes a user would like to taste rather than using the name of food facilities or their general categories.

Introduction
In late 2000's, we assisted to the explosion of TripAdvisor, the world's largest travel site, which offers advice about hotel and restaurants. In few years, it has revolutionized the restaurant industry, allowing its users to search restaurants by location, broad food categories (e.g., Mexican, Italian, French), reviews and ratings provided by other users. However, the user expectation has evolved overtime: looking for restaurants is not enough anymore, people are now considering finer-grained properties of food, e.g., a particular way to cook a dish along with its specific ingredients. Thus, there is a clear gap between what the market proposes and the emerging trends. In this work, we present Appetitoso, a search engine that seeks for restaurants based on dishes. This approach is designed to help users to find their restaurants having already a specific dish preference in mind, using fine-grained properties of the dish. Appetitoso integrates state-of-the-art search engines, such as BM25, with a domain specific knowledge base describing properties and similarity relations between different Italian dishes. This knowledge is very useful, e.g., in our experiments, we show that it greatly boosts dish retrieval. Appetitoso is available as a mobile phone application (e.g., Android and iOS) and website, released in 2014 for two languages, English and Italian. It is an end-to-end application for finding restaurants offering the desired dish. We evaluated it using a set of 547 popular queries typed by its users in the cities of Rome, Milan and Florence. In the reminder of this paper, in Section 2, we report related work on systems for automatic food recommendation, In Section 3, we introduce Appetitoso, its knowledge base and the food search engine. Section 4, we describe our experiments on restaurant retrieval on Italian language and finally, in Section 5, we provide our conclusion.

Conclusion
In this paper we presented Appetitoso, a semantic search engine for food. The aims of the search engine is to provide the users with a way of searching restaurants by dishes rather than just using the restaurants' address or cuisine type. We show that, given the complexity of dish naming, a semistructured database for dishes can largely improve BM25. Overall, Appetitoso shows good performance, e.g., achieving 88.76\% in MAP. In the future, we would like to include more complex unstructured data such as the description of the dishes and also explore the possibility of word embeddings for the food domain. Moreover, it is also important increase the coverage of the system by adding more dishes to the FKB. Even if the manual annotation is important, and in some cases fundamental, it represents a bottleneck for the expansion process. For this reason, in the future it would be necessary consider approaches to automatically extract dish entities from text (e.g. NER for food). We would like to thank the Appetitoso team for making available the system and for providing us with the data for this work. This work has been partially supported by the EC project CogNet, 671625 (H2020-ICT-2014-2, Research and Innovation action) and by an IBM Faculty Award. The first author was supported by a fellowship from TIM. Many thanks to the anonymous reviewers for their valuable suggestions. Although, ours is a rather particular retrieval task, there \alex{is no relevant differences in how users interact with expert search systems}. In Fig.~fig:tokens, we show that most queries have a length less than 4 words with an average of 3.7 words. This is in line with other standard retrieval tasks AUTHOR. In order to make them comparable, we normalized the three different sets by the number of the queries. \alex{I do not get the aim of this graph} \alex{In contrast, we do not have the accuracy without FKB, which is the most interesting part of this paper.} [htb!] \centering \includegraphics[width=1\linewidth]{img/chart.pdf}