Towards Recommender Systems in Augmented Reality for Tourism

Recent advances in augmented reality have enabled new ways of generating and presenting item recommendations. In tourism, AR applications can, for example, enhance points of interests (POIs) with virtual elements in AR and provide tourists with personalized recommendations for places to visit. In this paper, we present our prototype: a touristic AR application that augments various POIs with digital content and generates context-aware recommendations for POIs in the Niederdorf old town of Zurich, Switzerland. We demonstrate how useful information can be presented to users in an engaging way by combining AR technologies and recommender systems.ISSN:2198-724

Talking Houses: Transforming Touristic Buildings into Intelligent Characters in Augmented Reality

Augmented reality (AR) technologies can enhance the user's experience of visiting attractions, shops, and restaurants by using AR-based virtual elements and additional information about the places they are visiting. In this work, we transform the city landscape or iconic buildings into a unique experience by bringing iconic characters onto the buildings to increase users' engagement. Our techniques transform buildings or parts of a building into a virtual character with which the user can interact. We designed two unique experiences: (a) 'The Square' in which the character will talk about the building's history and other anecdotes about the area, and (b) 'The Hunt' in which the user is involved in a scavenger hunt where they have to identify buildings using the hints given by virtual characters. We have conducted a live user study to assess our prototype's usability. Our preliminary experimental results demonstrated that our prototype has high usability and users using our system felt a pleasant and enjoyable experience.ISSN:2198-724

RecSys Challenge 2022 Dataset: Dressipi 1M Fashion Sessions

As part of the RecSys Challenge 2022, the Dressipi 1M Fashion Sessions dataset is publicly released. This paper gives an overview of the content and structure of the dataset, as well as explaining the process by which it was constructed. The dataset contains anonymous browsing sessions, a purchase for each session, as well as content data of the items. The content data consists of IDs that represent descriptive fashion characteristics of the items and have been assigned using Dressipi's human-in-the-loop labelling system. We hope that this dataset will be valuable in recommender systems research beyond the RecSys Challenge and encourage more publications in the fashion domain