A BERT-Based Sequential POI Recommender system in Social Media

Noorian, A.

doi:10.1016/j.csi.2023.103766

Cited by 11 publications

(1 citation statement)

References 25 publications

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“…Although existing RSs achieve high accuracy through backtracking tests, considering accuracy alone may lead to the phenomenon of "filter bubbles" [60], which isolates consumers from a diversity of content. Moreover, the accuracy inferred from historical records may not reflect the real correlations among products very well when "concentration bias" is heavy [65].…”

Section: Rs Limitationsmentioning

confidence: 99%

Fog Computing-Based Smart Consumer Recommender Systems

Hornik,

Ofir,

Rachamim

et al. 2024

JTAER

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The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

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Section: Rs Limitationsmentioning

confidence: 99%