BOTTARI: An augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams

Abstract

In 2011, an average of three million tweets per day was posted in Seoul. Hundreds of thousands of tweets carry the live opinion of some tens of thousands of users about restaurants, bars, cafes, and many other semi-public points of interest (POIs) in the city. Trusting this collective opinion to be a solid base for novel commercial and social services, we conceived BOTTARI: an augmented reality application that offers personalized and localized recommendation of POIs based on the temporally weighted opinions of the social media community. In this paper, we present the design of BOTTARI, the potentialities of semantic technologies such as inductive and deductive stream reasoning, and the lessons learnt in experimentally deploying BOTTARI in Insadong–a popular tourist area in Seoul–for which we have been collecting tweets for three years to rate the hundreds of restaurants in the district. The results of our study demonstrate the feasibility of BOTTARI and encourage its commercial spread.

Introduction

Imagine that you are a tourist in Seoul. You would like to dine out. You prefer to avoid tourist traps and dine where the locals do. You have been told that Insadong district would be the perfect place; it offers a choice of more than a hundred restaurants in two square kilometres, and most of the district is reserved for pedestrians.

When you reach Insadong-gil (the main street of the district), you find yourself surrounded by hundreds of restaurant advertisements (see Fig. 1). You know that you can still open your guide book and choose one of the few restaurants listed there, but you definitely want a place where the locals go. You take out your mobile and check various apps that recommend restaurants based on users’ reviews. The number of user-rated restaurants is smaller than you expected: only ten restaurants are rated more than ten times. This is probably because you are in Seoul, one of the cities world-wide in which people tweet a lot.¹ You wish that a service existed that continuously analysed the social media streams and that could show you how the locals have been rating Insadong’s restaurants over the last few months.

This is exactly what we designed BOTTARI for. BOTTARI is an augmented reality application for personalized and localized restaurant recommendations, experimentally deployed in the Insadong district of Seoul. At first glance, it may appear like other mobile apps that recommend restaurants, but BOTTARI is different: BOTTARI uses inductive and deductive stream reasoning [1] to continuously analyse social media streams (specifically Twitter) to understand how the social media users collectively perceive the points of interest (POIs) in a given area, e.g., Insadong’s restaurants.

In this paper, we describe the choices we made in designing BOTTARI and the lessons we learned by experimentally deploying it in Insadong. The paper is organized as follows. Section 2 introduces relevant background. Section 3 illustrates the BOTTARI mobile app from the user’s point of view, i.e., the main task being pursued. Section 4 explains how to understand the data used in experimentally deploying BOTTARI in Insadong district. Section 5 briefly illustrates the ontology at the core of BOTTARI that was used to integrate the available information. Section 6 presents the BOTTARI back-end. Sections 7 Evaluation, 8 Scalability report our evaluation results both in terms of quality of recommendations and scalability of the BOTTARI back-end. Finally, Section 9 concludes the paper by discussing the lessons we learnt and sketches our future works.