A Markov Chain Model for Integrating Context in Recommender Systems

Mourchid, Fatima; Othman, Jalel Ben; Kobbane, Abdellatif; Sabir, Essaïd; Koutbi, Mohammed El

doi:10.1109/glocom.2016.7841514

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In Zhao, King, and Lyu (2016) three main challenges are identified for these type of systems: physical constraints ( Liu & Xiong, 2013 ), complex relations between data ( Baral and Li, 2017 , Hosseini and Li, 2016 ), and heterogeneous information ( Wang, Li, Liu, & Shao, 2021 ). Others focus on the context integration to improve the quality of recommendations as in Habayeb, Soltanifar, Caglayan, and Bener (2016) and Mourchid, Othman, Kobbane, Sabir, and Koutbi (2016) . While others address specific problems, for instance, in Xia, Li, Li, and Li (2017) neural networks are applied to generate a sequence of recommendations, or in Han and Yamana (2020) the authors seek to diversify the POI recommendations to include new places and those that are not often visited.…”

Section: State Of the Artmentioning

confidence: 99%

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Crowd management COVID-19

Polanco¹,

Siller²

2021

Annual Reviews in Control

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Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.

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Section: State Of the Artmentioning

confidence: 99%

“…Following with CARS, the work in Mourchid et al (2016) presents a recommender system that utilizes a Markov chain to predict contextual information and recommend places that can be visited at the next interval of time. In this case, contextual information refers to crowdedness.…”

Section: State Of the Artmentioning

confidence: 99%