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

Abstract

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

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

“…Massimo et al [5] leveraged Inverse Reinforcement Learning method to learning user preferences by observing tourists onsite behavior in an IoT-equipped smart museum so as to predict next exhibit sequentially for tourists. Hashemi et al [6,7] solved the challenging next POI recommending problem by logging and mining users' onsite physical and online interaction behavior data within an IoT-augmented museum. However, the above works fail to generate personalized and tangible travel routes for tourists.…”

“…POIs) based on one's personal preference and constraints. Recently, industry and academia have been studying and developing tourism recommendation systems for providing convenient travel information to tourists, including next POI suggestion [1][2][3][4][5][6][7], Top-k POIs recommendation [8][9][10], and POIs travel route recommendation [11][12][13][14][15][16][17][18]. Particularly, travel route recommendations are more practical and useful than the two former kinds of POI recommendations in practice, yet they are more challenging.…”

The Future of Emerging IoT Paradigms: Architectures and Technologies

“…The environment is at a museum and seeks to monitor the behavior of attendants based on their paths through the exhibits and their interactions with the Points of Interest. The author in [68] refers to IoT as a network of connected physical objects, in which sensors and actuators are seamlessly embedded in physical environments, and information is shared across platforms to develop a common operating picture. This definition of IoT aligns quite well with the architectures we have discussed so far and manages to encapsulate other architectures we have not.…”

“…This definition of IoT aligns quite well with the architectures we have discussed so far and manages to encapsulate other architectures we have not. Conveniently, [68] contains a reference to research done by Evangelatos et. al.…”

“…al. [54] which elaborates on a wireless IoT network that is most relevant to the IoT network in [68]. This network is fitted with wireless sensors and actuator nodes that run on an IPv6 6LoWPAN network.…”