An intelligent recommendation system using gaze and emotion detection

Jaiswal, Saurabh; Virmani, Shubham; Sethi, Vishal; De, Kajal; Roy, Partha Pratim

doi:10.1007/s11042-018-6755-1

Cited by 36 publications

(14 citation statements)

References 20 publications

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“…Based on the user's needs, the data expression is used to deal with the modeling problem between the user and the resource, and then the neighbor users are calculated based on the similarity of the user's behavior. Finally, the resource with the highest evaluation is found from the neighbor users and recommended to the current user [11].…”

Section: Common Methods Of the Intelligent Recommendationmentioning

confidence: 99%

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Cui

2021

Mathematical Problems in Engineering

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With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

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Section: Common Methods Of the Intelligent Recommendationmentioning

confidence: 99%

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Cui

2021

Mathematical Problems in Engineering

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“…Still, biometric and behavioral measures other than ET are nowadays available to investigate emotions. In this context, the recalled facial expression recognition systems are clearly an increasingly viable option within design [97,98] and product evaluation [99], while the chance to combining them with ET is under investigation [100].…”

Section: Discussionmentioning

confidence: 99%

Observing Pictures and Videos of Creative Products: An Eye Tracking Study

2020

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The paper offers insights into people’s exploration of creative products shown on a computer screen within the overall task of capturing artifacts’ original features and functions. In particular, the study presented here analyzes the effects of different forms of representations, i.e., static pictures and videos. While the relevance of changing stimuli’s forms of representation is acknowledged in both engineering design and human-computer interaction, scarce attention has been paid to this issue hitherto when creative products are in play. Six creative products have been presented to twenty-eight subjects through either pictures or videos in an Eye-Tracking-supported experiment. The results show that major attention is paid by people to original product features and functional elements when products are displayed by means of videos. This aspect is of paramount importance, as original shapes, parts, or characteristics of creative products might be inconsistent with people’s habits and cast doubts about their rationale and utility. In this sense, videos seemingly emphasize said original elements and likely lead to their explanation/resolution. Overall, the outcomes of the study strengthen the need to match appropriate forms of representation with different design stages in light of the needs for designs’ evaluation and testing user experience.

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“…Only [11] proposed a system to support UX assessment based deep learning, but such a solution works only for mobile applications. While only [7] proposed to process data provided by a common pc webcam to enable users' gaze and emotion detection in order to manage an intelligent e-commerce recommendation system: it proposed respectively to use SVM algorithm to enable emotion recognition and a gradient-based method to perform gaze tracking. These algorithms, compared with most of Deep Learning CNNs, are more efficient for what concerns the computational performance, but less accurate [16].…”

Section: Research Backgroundmentioning

confidence: 99%

“…Moreover, recent years have shown an increasing interest in enhancing possibilities of human-computer interaction with ecommerce. Recommendation systems have started to attract interest in both business and research [7]. In the last few years there was significant improvement especially over the collaborative filtering approaches thanks to the advance in the field of machine learning and deep learning techniques [8].…”

Section: Introductionmentioning

confidence: 99%

“…In the last few years there was significant improvement especially over the collaborative filtering approaches thanks to the advance in the field of machine learning and deep learning techniques [8]. However, the effectiveness of such a system remains generally low, as the algorithms used by most of the web e-commerce for providing shopping suggestions to customers use data collected from other customers, so that system difficulty meets the ASTESJ ISSN: 2415-6698 customer's expectation [7]. In this context, emotion detection represents one of the directions that can be taken to enhance the overall UX with these systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild

Generosi¹,

Ceccacci²,

Faggiano³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Nowadays, smartphones and laptops equipped with cameras have become an integral part of our daily lives. The pervasive use of cameras enables the collection of an enormous amount of data, which can be easily extracted through video images processing. This opens up the possibility of using technologies that until now had been restricted to laboratories, such as eye-tracking and emotion analysis systems, to analyze users' behavior in the wild, during the interaction with websites. In this context, this paper introduces a toolkit that takes advantage of deep learning algorithms to monitor user's behavior and emotions, through the acquisition of facial expression and eye gaze from the video captured by the webcam of the device used to navigate the web, in compliance with the EU General data protection regulation (GDPR). Collected data are potentially useful to support user experience assessment of web-based applications in the wild and to improve the effectiveness of e-commerce recommendation systems.

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An intelligent recommendation system using gaze and emotion detection

Cited by 36 publications

References 20 publications

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Observing Pictures and Videos of Creative Products: An Eye Tracking Study

A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild

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