Effects of Position and Time Bias on Understanding Onsite Users' Behavior

Hashemi, Seyyed Hadi; Hupperetz, W.; Kamps, Jaap; Vaart, Merel van der

doi:10.1145/2854946.2855004

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Analysis of this data can deliver insights into how visitors move within a museum and what they look at. It can also reveal effects of factors such as age, language, and even time of day on the actions of visitors within a site [40]. This data can also offer useful insights on the design of an exhibition space, such as in the Atlantikwall exhibition [18], where analysis of visitor data showed a single exhibit that was almost completely ignored, due to it's location being obscured from visitors.…”

Section: Data Analytics For Heritage Sitesmentioning

confidence: 99%

“…This includes visit personalisation, online pre-and post-visit experiences, connections across different sites and visitor anayltics. So far, museums have primarily used either anonymous session-based identification and tracking [18], [37], or have required visitors to register an email address or account with them [40].…”

Section: B Visitor Identification and Trackingmentioning

confidence: 99%

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Interacting with Heritage: On the Use and Potential of IoT Within the Cultural Heritage Sector

Marshall

2018

2018 Fifth International Conference on Internet of Things: Systems, Management and Security

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Section: Data Analytics For Heritage Sitesmentioning

confidence: 99%

Section: B Visitor Identification and Trackingmentioning

confidence: 99%

Interacting with Heritage: On the Use and Potential of IoT Within the Cultural Heritage Sector

Marshall

2018

2018 Fifth International Conference on Internet of Things: Systems, Management and Security

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“…Another aspect of application of IoT to CH sites is the analytics of the data gathered by the installed devices [ 12 , 13 ]. The various IoT devices installed at such a site may include security cameras, temperature and ambience sensors, and logging systems for visitors and so on.…”

Section: Introductionmentioning

confidence: 99%

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

Tanwar

Chauhan

Yafi

2021

Sensors

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We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.

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“…The last decade witnessed a surge of interest in the implementation of Internet of Things (IoT) in different applications, such as smart shopping malls and smart museums, which provide the infrastructure for understanding users' physical interaction behavior and consequently their preferences in interacting with smart environments (Atzori, Iera, & Morabito, 2010;Barnaghi, Wang, Henson, & Taylor, 2012;Vermesan & Friess, 2013;Hashemi, Hupperetz, Kamps, & van der Vaart, 2016;Hashemi & Kamps, 2017a;Hernández-Muoz et al, 2011;Perera, Zaslavsky, Christen, & Georgakopoulos, 2014). This prompts a range of questions: In what ways can tracking people in their reallife behavior and trying to understanding their interaction behaviors be helpful?…”

Section: Introductionmentioning

confidence: 99%

Exploiting behavioral user models for point of interest recommendation in smart museums

Hashemi

Kamps

2018

New Review of Hypermedia and Multimedia

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The Internet of Things (IoT) holds the promise to blend realworld and online behaviors in principled ways, yet we are only beginning to understand how to effectively exploit insights from the online realm into effective applications in smart environments. Such smart environments aim to provide an improved, personalized experience based on the trail of user interactions with smart devices, but how does recommendation in smart environments differ from the usual online recommender systems? And can we exploit similarities to truly blend behavior in both realms to address the fundamental cold-start problem? In this article, we experiment with behavioral user models based on interactions with smart devices in a museum, and investigate the personalized recommendation of what to see after visiting an initial set of Point of Interests (POIs), a key problem in personalizing museum visits or tour guides, and focus on a critical one-shot POI recommendation task-where to go next? We have logged users' onsite physical information interactions during visits in an IoT-augmented museum exhibition at scale. Furthermore, we have collected an even larger set of search logs of the online museum collection. Users in both sets are unconnected, for privacy reasons we do not have shared IDs. We study the similarities between users' online digital and onsite physical information interaction behaviors, and build new behavioral user models based on the information interaction behaviors in (i) the physical exhibition space, (ii) the online collection, or (iii) both. Specifically, we propose a deep neural multilayer perceptron (MLP) based on explicitly given users' contextual information, and set-based extracted features using users' physical information interaction behaviors and similar users' digital information interaction behaviors. Our experimental results indicate that the proposed behavioral user modeling approach, using both physical and online user information interaction behaviors, improves the onsite POI recommendation baselines' performances on all evaluation metrics. Our proposed MLP approach achieves 83% precision at rank 1 on the critical one-shot POI ARTICLE HISTORY

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Effects of Position and Time Bias on Understanding Onsite Users' Behavior

Cited by 10 publications

References 5 publications

Interacting with Heritage: On the Use and Potential of IoT Within the Cultural Heritage Sector

Interacting with Heritage: On the Use and Potential of IoT Within the Cultural Heritage Sector

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

Exploiting behavioral user models for point of interest recommendation in smart museums

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