An <scp>EEG</scp>‐based neuro‐recommendation system for improving consumer purchase experience

Panda, Debadrita; Chakladar, Debashis Das; Rana, Sudhir; Parayitam, Satyanarayana

doi:10.1002/cb.2142

Cited by 9 publications

(8 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another EEG experiment (Raiesdana & Mousakhani, 2022) tested neural measures' ability to predict electric car consumer preference. Similar positive results were observed by another recent study on neuro‐recommendation systems for improving consumer purchase experience (Panda et al, 2023).…”

Section: Introductionsupporting

confidence: 88%

Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

Mashrur,

Rahman,

Miya

et al. 2023

J of Consumer Behaviour

View full text Add to dashboard Cite

Neuromarketing uses brain‐computer interface technology to understand customer preferences in response to marketing stimuli. Every year, marketing professionals spend over $750 Billion (US dollars) on traditional marketing, which is usually behavioral and subjective, focusing on self‐reports acquired via questionnaires, focus groups, and depth interviews. Neuromarketing, on the other hand, promises to overcome such limitations. This work proposes a machine learning framework that incorporates multiple components (endorsement, offer, and slogan) in real advertisement to predict consumer preference from electroencephalography (EEG) signals. In addition, we also use eye‐tracking data to visualize consumer viewing patterns according to both advertisement type and preference. EEG signals are collected from 22 healthy volunteers while viewing the real ads as stimuli. After preprocessing the signals, three‐domain features are extracted (time, frequency, and time‐frequency). Then, using wrapper‐based approaches we choose best features which are later classified into strong and weak preferences using the support vector machine. The experimental results demonstrate the best performance using all the frontal channels with an accuracy of 96.97%, sensitivity of 96.30%, and specificity of 97.44%. Additionally, eye tracking data reveals that subjects substantially prefer an ad, when they first glance at the endorsement. In addition, people tend to blink their eyes less frequently while viewing ads with endorsements and strongly prefer these commercials too. Additionally, our work lays the door for deploying such a neuromarketing framework in a real‐world context by employing consumer‐grade EEG equipment. Therefore, it is evident that neuromarketing technology may assist brands and companies in accurately predicting future customer preferences.

show abstract

Section: Introductionsupporting

confidence: 88%

Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

Mashrur,

Rahman,

Miya

et al. 2023

J of Consumer Behaviour

View full text Add to dashboard Cite

show abstract

“…Physiological and behavioral signals, such as EEG and gaze tracking, capture deeper user insights for personalized recommendations [85], [86], [87], [88], [89], [90]. These signals provide valuable information about users' cognitive and emotional states, allowing recommendation systems to tailor suggestions based on real-time user feedback and interactions.…”

Section: Discussionmentioning

confidence: 99%

A Survey of Latent Factor Models for Recommender Systems and Personalization

Shah¹

2021

Preprint

View full text Add to dashboard Cite

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

show abstract

“…"The latest technologies like Fog computing can be utilized to make a recommendation system faster and independent of internetwork connection" [20] (p. 13).…”

Section: Study Objectivesmentioning

confidence: 99%

“…Consumers expect personalized content in modern e-commerce, entertainment, and social media platforms. RSs comprise a subclass of information filtering systems, which identify and recommend items based on consumer tastes and preferences and seek to predict "rating" or "preferences" for an item not yet considered through a model built from item characteristics or the consumer's social environment [20,57] RSs have been established as a crucial solution to keep consumers engaged and satisfied with personalized content in addition to helping users navigate a vast array of choices. Felfernig and Burke (2020) define RSs as encompassing "[a]ny system that guides a user in a personalized way to interesting or useful objects in a very large space of possible options or that produces such objects as output" [58] (p. 1).…”

Section: Recommender Systems (Rss)mentioning

confidence: 99%

See 1 more Smart Citation

Fog Computing-Based Smart Consumer Recommender Systems

Hornik,

Ofir,

Rachamim

et al. 2024

JTAER

View full text Add to dashboard Cite

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.

show abstract

An EEG‐based neuro‐recommendation system for improving consumer purchase experience

Cited by 9 publications

References 70 publications

Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

A Survey of Latent Factor Models for Recommender Systems and Personalization

Fog Computing-Based Smart Consumer Recommender Systems

Contact Info

Product

Resources

About