Effective healthcare service recommendation with network representation learning: A recursive neural network approach

Ayadi, Mouhamed Gaith; Mezni, Haithem; Alnashwan, Rana; Elmannai, Hela

doi:10.1016/j.datak.2023.102233

Cited by 3 publications

(1 citation statement)

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“…Several recommendation approaches have been proposed in various smart city contexts, including smart health care, 7,25 smart markets, 8 smart transportation, 26,27 smart parking, 9,10 ride sharing, 11 e-governance, 12 smart mobility, 13 smart tourism services, and attractions [14][15][16] (see Table 2).…”

Section: Recommender Systems For Smart Citiesmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

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